An AI data semantic analysis method and system based on dialogue interaction

Abstract

This invention relates to the fields of artificial intelligence and natural language processing, specifically to an AI data semantic analysis method and system based on dialogue interaction. The invention synchronously acquires audio time-series signals and text data, establishes a coupled mapping relationship between bimodal data using a hardware clock reference and a semantic alignment algorithm; extracts physical representation vectors using adaptive suppression and time-frequency transformation, and combines this with NLP algorithms to extract core semantic features; constructs a hybrid model architecture integrating a Transformer codec, utilizes session state caching to build logical coherence representation vectors, achieves alignment and fusion of multimodal data in a high-dimensional space, and generates a global semantic representation vector; finally, it performs quantitative analysis from the dimensions of content, sentiment, and expressive characteristics, outputting a scenario-based digital evaluation report, effectively solving the problems of multimodal data distortion and semantic conflict, and improving the accuracy of logical monitoring.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and natural language processing technology, specifically to an AI data semantic analysis method and system based on dialogue interaction. Background Technology

[0002] With the deep integration of artificial intelligence technology and digital transformation, dialogue interaction has become the core form of data collection for human-computer interaction and interpersonal interaction. Massive amounts of unstructured dialogue data are generated daily in diverse scenarios such as AI-powered intelligent interviews in recruitment, voice and text feedback in customer service, and real-time teacher-student interaction in campuses and online education. This data not only contains explicit text content but also rich voice modal information such as speech rate, tone, and intensity, deeply implying the semantic logic, emotional tendencies, behavioral intentions, and potential needs of the dialogue subjects. It is a core data carrier for mining the value of different scenarios.

[0003] Existing semantic analysis technologies generally suffer from the dual drawbacks of modal fragmentation and insufficient scenario adaptability. They are largely limited to the independent processing of single-modal data: for example, traditional natural language processing only extracts keywords and performs simple logical structure analysis of text, or basic speech recognition technology simply captures acoustic features. They lack the ability to collaboratively integrate and analyze text and speech data, failing to achieve a synergistic interpretation of "semantic content + speech emotion." Furthermore, existing methods are insufficient in capturing the contextual relevance of multi-turn dialogues, making it difficult to effectively decipher semantic ambiguities, emotional shifts, and implied intentions in conversations—such as vague statements about professional experience by candidates in job interviews or indirectly expressed dissatisfaction in customer reviews, all of which are difficult to accurately interpret through single-modal or simple contextual analysis. In addition, existing technologies have extremely poor scenario adaptability, unable to customize analysis dimensions and weights according to the core needs of different scenarios. For example, job interviews require a focus on assessing the candidate's competency matching the position, customer reviews require focusing on satisfaction levels and extracting core needs, and teaching interactions require accurately capturing students' knowledge mastery and classroom participation quality. Existing technologies struggle to address these differentiated needs, leading to a significant decrease in the practicality of the analysis results.

[0004] Furthermore, traditional dialogue analysis methods often rely on manually setting fixed evaluation rules and thresholds, which is not only highly subjective, costly, and inefficient, but also unable to adapt to dynamic changes in dialogue data and iterative scenarios, making it difficult to achieve real-time output of analysis results and dynamic model optimization. This lag and limitation can no longer meet the intelligent upgrade needs of enterprises for improved recruitment screening efficiency, real-time customer service response, and dynamic adjustment of the teaching process. Therefore, there is an urgent need for an AI data semantic analysis method and system that can deeply integrate text and speech dual-modal data, strengthen multi-turn contextual semantic associations, and accurately adapt to the needs of multiple scenarios to fill the gaps in existing technology.

[0005] To address this, an AI data semantic analysis method and system based on dialogue interaction is proposed. Summary of the Invention

[0006] This invention aims to provide an AI data semantic analysis method and system based on dialogue interaction. By constructing a dual-modal fusion analysis framework and a contextual semantic modeling system, it achieves multi-dimensional, in-depth, and scenario-based semantic analysis of dialogue data (text + speech), significantly improving the accuracy, comprehensiveness, and practicality of the analysis results. It provides efficient and reliable data support for intelligent decision-making in scenarios such as recruitment interviews, customer evaluations, and teaching interactions, and helps digital upgrades in various fields.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for AI data semantic analysis based on dialogue interaction, comprising: Acquire audio timing signals and text data during the interaction process; extract the physical representation parameters of the audio timing signals and map them into structured text with timestamps; simultaneously convert the text data into a standard encoding format and associate it with scene labels; establish a coupling mapping relationship between audio timing signals and text data on the time axis to form a multimodal raw dataset. Based on the aforementioned multimodal raw dataset, natural language algorithms are used to perform serialization annotation and redundant information filtering on the text data to extract core semantic features; simultaneously, an adaptive noise suppression algorithm is used to perform signal purification on the audio time-series signal, and physical representation vectors containing rhythm features, frequency fluctuations, and energy intensity are extracted through time-frequency transformation; A hybrid model architecture integrating a bidirectional Transformer encoder and decoder is constructed. The encoder extracts bidirectional contextual features of the text for semantic disambiguation, and the decoder's session state cache dynamically constructs a logical coherence representation vector for multi-turn interactions. The physical representation vector is projected onto the semantic space and fused with the logical coherence representation vector in dimension alignment to generate a global semantic representation vector. Perform in-depth quantitative analysis and generate reports.

[0008] Preferably, establishing the coupling mapping relationship between audio timing signals and text data on the time axis includes: The hardware abstraction layer of the integrated acquisition module synchronously acquires the clock reference of the audio data stream and the interrupt signal of the text input system, and uses a concurrent processing mechanism to segment the audio data into time-domain acoustic segments; during the extraction of physical representation parameters, the alignment between the text representation and the original waveform envelope is calculated based on the dynamic time warping algorithm to calibrate the timing deviation caused by data encoding delay. For manually entered non-real-time text, semantic similarity retrieval is used to locate its logical mapping point in the interactive semantic stream. Then, the sliding window technique is used to perform secondary clustering mapping between the non-real-time text and the physical representation vector of the corresponding time period to construct a structured original dataset with an asynchronous compensation mechanism.

[0009] Preferably, the extraction of core semantic features includes: The corresponding domain knowledge base is retrieved based on scene tags, and domain weight labeling is performed on the word segmentation results; statistical models are used for syntax annotation and dependency parsing trees are constructed to identify and retain restrictive modifiers with semantic guidance. When filtering redundant information, the energy density distribution and temporal length of candidate filter items in the original dataset are calculated to retain non-lexical physical features with semantic emphasis function, and a probabilistic prediction model is used to correct semantically ambiguous segments, thereby achieving high-fidelity semantic compression of interactive text.

[0010] Preferably, the step of performing signal purification and extracting physical representation vectors from the audio temporal signal includes: The deep residual reduction network is used to identify non-stationary interference substrates in the interactive environment and perform nonlinear suppression to extract the purified physical envelope signal. When performing time-frequency transformation, the step size of the analysis window is dynamically adjusted according to the speech rate parameter of the interactive subject to capture the starting position and pause interval of the signal. A multi-scale attention mapping model is constructed to perform normalized alignment between the audio frequency fluctuation curve and the energy distribution feature, generating a dynamic feature operator that represents the interactive rhythm. The dynamic feature operator is then used to perform confidence weighting on the speech rate feature to eliminate feature distortion caused by modal overlap.

[0011] Preferably, the hybrid model architecture and the dynamically constructed logical coherence representation vector include: A cross-attention operator is introduced into the encoder to inject feature vectors from multiple rounds of historical interactions into the representation process of the current text, thereby achieving semantic completion of indicator pronouns and omitted components. A multi-dimensional session state cache is constructed using the hidden layer state of the decoder, and the historical semantic vector is iteratively updated using a weight decay operator to identify logical transition signals of interactive topics. The logical deviation rate of the current interaction relative to the historical context is quantified by calculating the cosine offset between the current vector and the aggregated vector of the session state cache, and the logical deviation rate is incorporated into the logical coherence representation vector as a constraint term.

[0012] Preferably, the generation of the global semantic representation vector includes: By using a cross-modal projection matrix based on a gating mechanism, the variable-length physical representation vector is mapped to a high-dimensional space with the same dimension as the logical coherence representation vector, so as to eliminate the dimensional differences between the physical modality and the text modality. During the fusion process, mutual information entropy is used to calculate the consistency probability between physical representation and text semantics. When a conflict is found between the emotional intensity expressed by physical features and the literal semantics of the text, a dynamic weighting operator is used to adjust the feature contribution of different modalities, and a nonlinear fusion is performed through a collaborative attention mechanism to generate a global semantic representation vector containing non-linguistic expression details and deep contextual features.

[0013] Preferably, the process of performing in-depth quantitative analysis and outputting a report includes: Content semantic quantitative analysis: Based on scene tags, knowledge graphs are invoked to quantify the distribution of core semantics, and text similarity algorithms are used to determine the completeness of the logical structure, extract explicit needs and implicit intentions, and generate content dimension scores; Interactive sentiment cross-analysis: Construct a two-dimensional sentiment mapping matrix, perform temporal distribution consistency verification between the sentiment labels mapped from physical features and the semantic polarity of the text; perform weighted calibration on attribute conflict fragments through sentiment offset correction operator, and output a comprehensive sentiment quantification value; Routine assessment of expressive traits: compare speech rate, pause ratio, and frequency fluctuation amplitude with the scene baseline threshold to calculate the fluency index and interaction rhythm frequency; Based on the scene label matching weight coefficient, the results of each dimension are weighted and summed to obtain the total scene evaluation score. Based on the scoring deviation, the suggestion library is retrieved to match improvement measures, and an evaluation report containing a multi-dimensional distribution map, key node index and digital improvement strategy is generated.

[0014] An AI data semantic analysis system based on dialogue interaction, comprising: Acquisition module: Acquires audio time-series signals and text data during the interaction process; extracts the physical representation parameters of the audio time-series signals and maps them into structured text with timestamps; synchronously converts the text data into a standard encoding format and associates it with scene tags; establishes a coupling mapping relationship between audio time-series signals and text data on the time axis; and forms a multimodal raw dataset. Feature extraction module: Based on the multimodal original dataset, natural language algorithms are used to perform serialization annotation and redundant information filtering on the text data to extract core semantic features; simultaneously, an adaptive noise suppression algorithm is used to perform signal purification on the audio time-series signal, and physical representation vectors containing rhythm features, frequency fluctuations and energy intensity are extracted through time-frequency transformation; Semantic analysis module: Constructs a hybrid model architecture that integrates bidirectional Transformer encoder and decoder. The encoder extracts bidirectional contextual features of the text for semantic disambiguation. The decoder uses session state caching to dynamically construct logical coherence representation vectors for multi-turn interactions. The physical representation vectors are projected onto the semantic space and fused with the logical coherence representation vectors in dimension alignment to generate a global semantic representation vector. Analysis Report Module: Performs in-depth quantitative analysis and outputs a report.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Dual-modal data fusion analysis enhances the comprehensiveness and accuracy of analysis: Breaking through the limitations of existing single-modal analysis, it innovatively achieves deep fusion and cross-validation of text and speech dual-modal data, not only interpreting the semantic content of dialogue, but also capturing the emotional and expressive features at the speech level, realizing a comprehensive analysis of "semantics + emotion + expression".

[0016] 2. Strengthen contextual semantic association and resolve ambiguity and logical break problems: Adopting the BERT-GPT hybrid model architecture, it takes into account both bidirectional contextual understanding and dynamic maintenance of conversation state, which can effectively capture semantic ambiguity and topic switching in multi-turn dialogues and avoid logical deviations caused by isolated analysis of single sentence semantics.

[0017] 3. Standardization and Objectivity Enhance the Scientific Nature of Decision-Making: By constructing a multi-dimensional indicator quantitative evaluation system based on normalization processing, standardization and objectivity in the decision-making process are achieved. Feature extraction operators are used to perform multi-dimensional mapping on heterogeneous scenario data, and a pre-set weighted decision model is used to perform refined quantitative analysis of each indicator. By introducing a confidence verification mechanism and a non-linear evaluation function, objective data support with mathematical consistency is provided for complex business scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the AI ​​data semantic analysis method based on dialogue interaction of the present invention. Figure 2 This is a schematic diagram of the process by which the present invention performs in-depth quantitative analysis and outputs a digital analysis report; Figure 3 This is a schematic diagram of the AI ​​data semantic analysis system based on dialogue interaction according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figures 1 to 3 This invention provides an AI data semantic analysis method and system based on dialogue interaction, referring to... Figure 1 Flowchart and Figure 3 The structural diagram and technical solution are as follows: Acquire audio timing signals and text data during the interaction process; extract the physical representation parameters of the audio timing signals and map them into structured text with timestamps; simultaneously convert the text data into a standard encoding format and associate it with scene labels; establish a coupling mapping relationship between audio timing signals and text data on the time axis to form a multimodal raw dataset. Based on the aforementioned multimodal raw dataset, natural language algorithms are used to perform serialization annotation and redundant information filtering on the text data to extract core semantic features; simultaneously, an adaptive noise suppression algorithm is used to perform signal purification on the audio time-series signal, and physical representation vectors containing rhythm features, frequency fluctuations, and energy intensity are extracted through time-frequency transformation; A hybrid model architecture integrating a bidirectional Transformer encoder and decoder is constructed. The encoder extracts bidirectional contextual features of the text for semantic disambiguation, and the decoder's session state cache dynamically constructs a logical coherence representation vector for multi-turn interactions. The physical representation vector is projected onto the semantic space and fused with the logical coherence representation vector in dimension alignment to generate a global semantic representation vector. Perform in-depth quantitative analysis and generate reports.

[0021] Example 1: This embodiment is set in an AI-driven remote recruitment interview scenario. The hardware abstraction layer of the integrated acquisition module acquires the candidate's original audio PCM data stream with a sampling frequency of 16kHz and a quantization bit depth of 16bit. Simultaneously, the real-time evaluation string entered by the interviewer on the text input terminal is captured through the system interrupt instruction. The two heterogeneous data streams are marked with an initial timestamp with an accuracy of 10 milliseconds using a unified Coordinated Universal Time clock reference.

[0022] This invention focuses on improving the data alignment, feature extraction, and semantic modeling processes of computer systems when processing multimodal dialogue data, thereby enhancing the accuracy and robustness of automatic semantic analysis. The natural language algorithm is used to perform word segmentation, part-of-speech tagging, and syntactic dependency analysis on interactive text. It should at least include three operations: word segmentation of the text sequence, assigning part-of-speech tags to each segmented item, and constructing a dependency tree. The algorithm can be implemented using a statistical learning model or a sequence labeling model based on a deep neural network. However, in all cases, it should output a structured semantic feature sequence containing the word segmentation results and corresponding part-of-speech information. The adaptive noise suppression algorithm should at least dynamically adjust the noise suppression parameters based on the signal-to-noise ratio or environmental noise estimation results to separate or weaken non-stationary background noise from the speech signal. It can be implemented using spectral subtraction, adaptive filtering, or deep neural network estimation, but should ensure that the spectral distortion of the output signal does not exceed a preset threshold. The deep quantization analysis should at least be based on a global semantic representation vector and a preset scene evaluation model, calculating a score for at least one of the following: content semantics, consistency, or expression features, and mapping the score results to a structured digital evaluation result.

[0023] Furthermore, the acquired long audio data stream is segmented into 20-millisecond temporal acoustic segments using a concurrent processing mechanism. A pre-built 1D-CNN convolutional neural network model is then invoked to extract the physical representation parameters of each acoustic segment. This model architecture consists of one input layer, three convolutional layers, one global average pooling layer, and one fully connected layer. The convolutional layers are configured with 32, 64, and 128 convolutional kernels of size 3, respectively. The Adam optimizer performs nonlinear mapping on the parameter matrix after 500 iterations of training, outputting a 128-dimensional physical representation feature vector that includes pitch fluctuations, zero-crossing rate, and Mel-frequency cepstral coefficients.

[0024] Specifically, the system uses a dynamic time warping algorithm to calibrate the approximately 150-millisecond timing deviation caused by the ASR transcription engine encoding process. It constructs a cumulative cost matrix of dimension M×N, where M is the number of audio feature frames and N is the number of text tags. The Euclidean distance between the audio MFCC vector and the text phoneme embedding vector is calculated as the local cost. Dynamic programming logic is used to search for the optimal warping path in the matrix space that minimizes the cumulative residual, thereby accurately aligning the physical representation feature vector to the corresponding text time axis node.

[0025] Furthermore, for non-real-time notes entered by the interviewer within 30 seconds after the question and answer session, the system initiates an asynchronous compensation program based on semantic similarity retrieval, which calls a semantic alignment model based on the Transformer architecture. This model contains 6 encoder units, each with 8 self-attention heads and a hidden layer dimension of 512. It is pre-trained on a benchmark database containing 100,000 sets of interview corpora using the Adam optimizer.

[0026] Specifically, the semantic alignment model converts non-real-time text into a 512-dimensional semantic embedding vector and sets a sliding window with a span of 60 seconds in the interactive semantic stream. It uses the cosine similarity operator to calculate the association weight score between the non-real-time text vector and each transcribed text vector in the window. When the score exceeds 0.85, the logical mapping point is locked.

[0027] The threshold (0.85) is the optimal critical point determined by experimental simulations based on a preset validation set under different semantic distributions. Through similarity backtesting of semantic embedding vectors from a massive corpus, it was found that at a threshold of 0.85, the model can effectively balance precision and recall, ensuring high confidence in the logical alignment of strongly related semantic segments while filtering out background noise interference. Those skilled in the art can make slight adjustments based on the above empirical value according to the corpus characteristics of specific business scenarios. When the semantic similarity score reaches or exceeds the preset threshold (e.g., 0.85), the corresponding time segment is determined as the logical mapping point of the non-real-time text. Those skilled in the art can adjust this threshold within a reasonable range according to actual business scenarios, corpus distribution, or model accuracy requirements without affecting the implementation principle of this invention.

[0028] Furthermore, the system performs a secondary clustering mapping operation on the physical representation vectors near the locking point, and uses the K-means clustering algorithm to calculate the Euclidean distance between the speech energy distribution and the text semantic centroid in the 512-dimensional feature space. By associating non-real-time text with the audio segment closest to the cluster centroid, the spatiotemporal breaks caused by the lag in manual input are filled in.

[0029] Specifically, the aligned audio physical features, real-time transcribed text, non-real-time annotated text, and scene tags identified as recruitment interviews are subjected to tensor concatenation and JSON serialization to generate a structured raw dataset containing candidate unique identifiers, stage codes, and synchronization time series matrices. This dataset is then sent to the subsequent contextual semantic modeling module, completing the deep coupling mapping of multimodal data in the physical and semantic dimensions.

[0030] By utilizing hardware-level clock synchronization and dynamic time warping algorithms, intermodal misalignment caused by device latency or network jitter is eliminated from the ground up. An asynchronous compensation mechanism is introduced to solve the logical connection problem between manually entered data and real-time signals, ensuring the spatiotemporal consistency of the original dataset and laying a high-precision benchmark for subsequent deep feature fusion.

[0031] By receiving a structured raw dataset containing candidate unique identifiers, recruitment interview identifiers, and a synchronization time series matrix output by the preceding acquisition module, the system extracts the business scenario code from the scenario label field and retrieves the recruitment domain expert knowledge base pre-installed in local non-volatile memory. This knowledge base contains 5,000 core keywords and their associated semantic weights for specific industry skills, professional qualities, and behavioral motivations.

[0032] Furthermore, the transcribed text in the original dataset is atomically segmented using a word segmentation algorithm, and domain weight labeling is performed on the generated label sequence. This process adjusts the corresponding feature weights by calculating the hit probability of each word segment in the expert knowledge base.

[0033] Specifically, the tagging logic uses an augmented word frequency statistical model to multiply the initial weight of terms that match key positions such as "project management", "independent development" or "cross-departmental collaboration" by a scenario gain coefficient of 1.5, thereby significantly improving the expressive strength of core business words in the high-dimensional feature space of the text.

[0034] Furthermore, a syntax annotation was performed on the weighted word segmentation sequence using a syntax parsing model based on the Transformer architecture, and a dependency parsing tree was constructed. The model consists of four layers of symmetrical transformer encoding units, each layer is configured with four parallel self-attention heads, the hidden layer dimension is set to 256, and the weight matrix is ​​locked after 500 rounds of training on workplace corpus using the Adam optimizer.

[0035] Specifically, the parsing operator identifies restrictive modifier relationships in the text sequence by calculating the attention score matrix. For example, the system tracks the dependency path between the verb "responsible" and its modifier "comprehensive" to lock in and retain restrictive modifier operators that have semantic guidance, so as to ensure that key information on the depth of the candidate's responsibilities is not lost during the semantic compression process.

[0036] Furthermore, when performing redundant information filtering, the system performs physical feature verification on the candidate words such as "um" and "this" in the transcribed text, extracts the physical representation vector under the corresponding timestamp by indexing the original dataset, and calculates the normalized acoustic energy density value of 0.65 and the duration of 1.2 seconds for that period.

[0037] Specifically, the filtering logic performs a binary classification decision. If the peak acoustic energy gain of the interjection is lower than a preset 15% and the duration is less than 0.5 seconds, it is judged as meaningless noise and removed. If an energy mutation or an excessively long pause is detected, it is converted into a semantic emphasis label representing a state of mind or emotional stress and retained. This achieves high-fidelity semantic compression of interactive information in the text dimension.

[0038] Furthermore, a semantic probability model based on the BERT architecture is used to perform ambiguity correction processing on the filtered text segments. This model contains 12 self-attention layers and a hidden layer dimension of 768. By calculating the conditional probability distribution of candidate token words within the semantic window, semantic breakpoints caused by speech recognition deviations are identified and corrected.

[0039] Specifically, the correction algorithm masks ambiguous segments with probability scores below 0.12, calculates the dot product correlation of context word vectors using a bidirectional attention mechanism, selects the candidate word with the highest probability to complete text reconstruction, and finally outputs a set of core semantic feature vector sequences with 300 semantic nodes and a dimension of 512, which is then sent to the subsequent context semantic modeling module, thus achieving accurate distillation of cross-modal data at the semantic level.

[0040] Through a dual filtering mechanism of dependency parsing and acoustic energy verification, high-fidelity semantic compression is achieved. While eliminating redundant modal particles, physical features with emotional orientation and semantic emphasis are accurately preserved, avoiding the accidental deletion of key contextual information in traditional NLP preprocessing and improving the extraction quality of core information.

[0041] The raw audio temporal signal containing candidate speech features is obtained from the preceding integrated acquisition module. This signal is encapsulated in pulse code modulation format and has a sampling rate of 16000 Hz. It is then loaded into the adaptive noise suppression module as the initial input stream.

[0042] Furthermore, a deep residual reduction network is used to perform nonlinear purification processing on the input audio signal. This network model consists of a one-dimensional convolutional input layer, four stacked residual reduction blocks, a global average pooling layer, and a fully connected layer containing 128 neurons.

[0043] Specifically, each residual reduction block integrates a sub-network for automatically calculating the soft-threshold component. This sub-network performs absolute value operations and global mean extraction on the feature map, using two layers of fully connected neurons and a sigmoid activation function to generate a reduction coefficient between 0 and 1. The Adam optimizer then iterates through 1500 rounds on a sample set containing 5000 sets of complex industrial noise and interview background noise to lock the parameters. The soft-threshold component is essentially an adaptive noise filtering threshold. It dynamically generates a reduction coefficient by sensing the energy base of the background environment (such as air conditioner noise or electrical noise) in real time through the sub-network. When the signal amplitude is below the threshold, it is determined to be non-stationary interference and forcibly zeroed out (filtered out), while useful signals above the threshold (such as interview voices) are smoothly preserved. This mechanism utilizes the iterative learning of the Adam optimizer on thousands of noise samples to achieve a nonlinear filtering process that accurately extracts the purified speech envelope from the chaotic physical signal.

[0044] Furthermore, the deep residual reduction network extracts the purified physical envelope signal from signals containing air conditioning operating noise or equipment current noise by performing element-wise multiplication of the feature components of each channel with their corresponding reduction coefficients. This is achieved by forcing non-stationary interference components with amplitudes below the soft threshold base to be mapped to zero. The soft threshold base is essentially a preset or adaptively generated noise energy threshold. It acts as a background noise level in the signal processing space: the subnetwork learns from 5000 sets of industrial and environmental noise to determine the typical amplitude range of non-stationary interference such as air conditioning current noise and equipment operating noise; when the feature intensity of the real-time signal is below this base, it is judged as meaningless noise and triggers zero-mapping. This base is continuously corrected through 1500 iterations of the Adam optimizer, ensuring that the system can accurately identify and lock the physical boundary between useful speech and environmental background noise in complex and ever-changing interview environments.

[0045] Specifically, the system performs time-frequency transformation processing on the purified physical envelope signal. First, it maps the time-domain sampling points into a spectrum matrix containing 256 frequency points through the short-time Fourier transform operator, and simultaneously extracts the real-time estimated candidate speech rate parameters from the original dataset.

[0046] Furthermore, the system dynamically adjusts the step size of the analysis window based on the speech rate parameter. When the candidate's speech rate exceeds 180 characters per minute, the system automatically shortens the Hamming window length from 512 sampling points to 256 sampling points and adjusts the overlap to 128 sampling points accordingly.

[0047] Specifically, by using a sliding window scan with high temporal resolution to capture the energy start position in the audio signal and the logical gap of a single pause exceeding 3000 milliseconds, a time-frequency feature vector reflecting the distribution of speech pauses and sentence rhythm is generated.

[0048] Furthermore, a multi-scale attention mapping model is constructed to fuse the extracted audio frequency fluctuation curves and energy distribution features. The model architecture includes three parallel convolution channels with kernel sizes of 3, 5, and 7, respectively, to extract rhythmic features under different receptive fields.

[0049] Specifically, the multi-scale attention mapping model uses a weighted computation operator to perform a dot product operation on the frequency vector and the energy vector. By calculating the cross-modal correlation score matrix within each attention head, it generates a dynamic feature operator containing 128 components that represent the interactive rhythm.

[0050] Furthermore, the extracted speech rate features are weighted with confidence using the dynamic feature operator. By multiplying the original speech rate component by the probability weight value between 0 and 1 output by the dynamic feature operator, nonlinear correction is achieved for false speech rate fluctuations caused by consonant overlap or environmental echo within the feature domain.

[0051] Specifically, the system performs tensor alignment and splicing on the weighted speech rate features, frequency fluctuation features, and energy intensity features to synthesize the final 128-dimensional physical representation vector, and sends it in the form of a structured binary data stream to the subsequent context semantic modeling module, thus completing the digital flow process of audio signals from hardware perception to physical feature extraction.

[0052] Furthermore, the dynamic adjustment of the analysis window step size based on the speech rate parameters of the interactive subject includes: calculating the pitch period of the audio time-series signal using a short-time autocorrelation function, and using 1.5 to 2.5 times the pitch period as the initial window length; when the speech rate change rate is detected to exceed 15.0%, the overlap rate of the analysis window is simultaneously scaled proportionally to maintain the consistency of the sampling density of the feature vector in the time domain; in a recruitment interview scenario, when a candidate's speech rate suddenly increases due to nervousness, the system automatically shortens the STFT analysis window length to 20 milliseconds and increases the overlap rate to 75.0%, ensuring that the power spectrum envelope of high-frequency burst sounds is not smoothed, thereby accurately capturing the stress starting position and the stress characteristic parameters it carries; this solves the time-frequency resolution conflict problem caused by a fixed window length under non-stationary speech rates; and improves the stress positioning accuracy by more than 20.0%, significantly enhancing the objectivity of the quantification of expression fluency.

[0053] By employing a deep residual reduction network and dynamic window length adjustment technology, the robustness of the system in non-stationary noise environments is enhanced. By performing confidence weighting on speech rate through rhythm feature operators, feature distortion caused by far-field acquisition or signal overlap is effectively corrected, ensuring that the physical representation vector can truly reflect the expressive characteristics of the interactive subject.

[0054] The core semantic feature vector sequence containing 300 semantic nodes and with a dimension of 512 is obtained from the preceding semantic feature extraction module and loaded as the initial input stream into the constructed hybrid model architecture that integrates bidirectional Transformer encoder and decoder.

[0055] Furthermore, a bidirectional Transformer encoder unit with a 12-layer self-attention mechanism was constructed. Each layer of this unit is configured with 12 parallel attention heads. The number of neurons in the hidden layer is set to 768, and the Adam optimizer is used to complete the weight locking by training on 200,000 sets of multi-round interview dialogues with an initial learning rate of 0.0001.

[0056] Specifically, the system introduces a cross-attention operator within the encoder. By extracting the semantic embedding vectors generated from the previous three rounds of historical interactions as key and value vectors, and using the candidate's answer vector at the current moment as the query vector, the system calculates the distribution of the current text's relevance to the historical context by performing matrix multiplication of the query vector and key vector and dividing by a scaling factor of 8.7. This enables precise semantic completion mapping at the semantic level for demonstrative pronouns such as "it" and "that project," as well as for omissions of constituents.

[0057] Furthermore, a Transformer decoder unit with a 6-layer stacked structure is constructed. A multidimensional session state cache is dynamically constructed in the memory buffer using the tensor sequence with a dimension of 768 output from its hidden layer, and an exponential weight decay operator with a value of 0.95 is configured for this cache.

[0058] Specifically, the algorithm processing logic merges the newly generated semantic vectors in each round into the cache matrix, and uses the weight decay operator to perform a weighted accumulation operation on the old vectors with time step decay, to calculate a 768-dimensional aggregate vector that represents the overall semantic center of the current session. In this way, by capturing the movement trajectory of the aggregate vector in the feature space, the logical transition signal of the interaction topic from "personal introduction" to "technical solution" can be identified.

[0059] Furthermore, the system performs a quantitative calculation of the logical deviation rate by extracting the semantic feature vector generated at the current time step and the session state cache aggregation vector, performing a vector dot product operation in a 768-dimensional space, and dividing the dot product result by the product of the magnitudes of the two vectors to obtain a cosine similarity score between 0 and 1.

[0060] Specifically, the difference between 1 and the cosine similarity score is calculated using a hardware subtraction operator, yielding a logical deviation rate of 0.15 for the current interaction relative to the historical context. When this value exceeds a preset threshold of 0.4, the candidate's answer is deemed to have an irrelevant logical gap, and this value is incorporated as a constraint penalty into the generation operator of the logical coherence representation vector. This threshold (0.4) is determined based on the mutual information distribution characteristics generated by the large language model in the question-answering task. In the semantic relevance quantification system, 0.4 corresponds to an extremely low overlap between the candidate's answer content and the question keywords in the logical tensor (i.e., semantic overlap rate below 40%). Statistical backtesting on a large number of invalid answer samples revealed that when the relevance score falls below 0.4, the answer content exhibits significant randomness in its probability distribution, indicating an irrelevant answer. By introducing this deviation value into the generation operator as a constraint penalty, the logical coherence representation vector can be dynamically reduced, achieving adaptive negative feedback calibration for nonlinear logical gaps.

[0061] Furthermore, the system performs high-dimensional concatenation of the bidirectional context feature vector output by the encoder, the logical deviation rate constraint term, and the aggregated features cached by the decoder, and then performs dimensionality reduction processing through a fully connected mapping layer containing 512 neurons.

[0062] Specifically, the algorithm logic uses the Sigmoid activation function to map the above multidimensional features into a logical coherence representation vector containing 64 feature components. This vector is sent to the subsequent multidimensional semantic analysis module in the form of a structured binary data stream, providing underlying semantic model support for evaluating the rigor and consistency of the candidate's expression logic.

[0063] By utilizing session state caching and weight decay mechanisms, the model is endowed with long short-term memory capabilities and logical monitoring capabilities. By quantifying the logical deviation rate, real-time identification of topic shifts, semantic jumps, and logical breaks is achieved, significantly enhancing the system's reasoning coherence and disambiguation accuracy in complex multi-turn dialogue contexts.

[0064] Obtain the 128-dimensional physical representation vector output by the preceding audio suppression module, and the 64-dimensional logical coherence representation vector generated by the context semantic modeling module.

[0065] Furthermore, a cross-modal projection matrix based on a gating mechanism is used to perform dimension mapping processing. This projection matrix adopts a multilayer perceptron architecture containing 128 input neurons, 256 hidden layer neurons, and 64 output layer neurons. The logic is to nest a gating unit based on the sigmoid activation function at the output of each hidden layer neuron, and to nonlinearly scale the feature strength by calculating the activity score of the physical feature in the semantic space.

[0066] Specifically, the projection matrix performs a multiplication operation between a 128-dimensional physical representation vector and a 256×128-dimensional weight matrix, and uses a gating signal to perform element-wise masking on the calculated intermediate states, thereby transforming the pitch and energy features with acoustic dimensions into a 64-dimensional latent semantic vector with the same dimension as the text semantic vector, thus eliminating the dimensional differences between the audio modality and the text modality.

[0067] The semantic space refers to a vector space in which multimodal data is uniformly represented by a parameterized mapping function, and the same dimension and measurement method are used. The mapping function must at least satisfy the following: linear or nonlinear transformations are performed on the physical representation features and textual semantic features of the input, so that the distance or similarity between vectors in this space can reflect the semantic closeness of different interactive data.

[0068] The dimensional alignment fusion includes at least the following: summing the projected physical modality vector and the logical coherence vector element by weight, concatenating them, or reconstructing them by weight through an attention mechanism to obtain a fusion vector containing key information of the two modalities; in any implementation, the fusion result should be consistent with the input requirements of the subsequent quantitative analysis model in terms of dimension.

[0069] Furthermore, during the fusion process, mutual information entropy is used to calculate the consistency probability between physical representation and text semantics, and a neural network-based mutual information estimation model is constructed.

[0070] Specifically, the model contains two parallel feature extraction subnetworks, each consisting of two fully connected layers with 128 neurons per layer. ReLU is used as the non-linear activation function, and the Adam optimizer performs parameter updates at a learning rate of 0.001. The inputs are the aforementioned 64-dimensional latent semantic vector and 64-dimensional logical coherence representation vector, respectively.

[0071] Furthermore, by calculating the statistical dispersion between the product of the joint distribution and the marginal distribution of the two sets of feature vectors in the joint probability space, the system outputs a value between 0 and 1 as the consistency probability score.

[0072] Specifically, when physical features with drastic frequency fluctuations are detected in a candidate's speech, while the transcribed text shows a highly logical match of job skill keywords, the system uses a mutual information estimation model to identify an attribute conflict between the emotional stress state expressed by the physical features and the literal semantics of the text, and calculates and outputs a consistency probability score of 0.42 at this time.

[0073] Furthermore, when the consistency probability score is lower than a preset threshold of 0.5, a dynamic weighting operator is triggered to adjust the feature contribution of different modalities in real time. The 0.5 threshold is determined based on the logical neutrality of the binary classification probability distribution. In the multimodal feature fusion process, 0.5 represents the current model's significant loss of consistency in predicting a single modality (i.e., being in a state of semantic conflict or information ambiguity at its highest entropy). By setting this threshold as a trigger condition, when the consistency probability is lower than 0.5, it is determined that a single modality can no longer provide a reliable basis for judgment, and the dynamic weighting operator is forcibly activated. By redistributing the feature contribution of each modality, ambiguity is eliminated, ensuring the robustness of the overall mapping logic in complex corpus environments.

[0074] Specifically, the dynamic weighting operator uses a linear search algorithm to match adjustment instructions in a preset weight library based on a consistency probability score of 0.42, increasing the weight coefficient of physical representation reflecting the real stress resistance psychological state to 0.75, while decreasing the weight coefficient of semantic features of transcribed text to 0.25.

[0075] Furthermore, a nonlinear fusion operation is performed on the weighted features using a collaborative attention mechanism. This mechanism constructs a feature aggregation layer containing four parallel attention heads, each with a hidden feature dimension of 16.

[0076] Specifically, the algorithm logic calculates the importance score matrix of each dimension of the expression detail in the global recruitment context by performing the transpose dot product operation of the latent semantic vector and the logical coherence vector within each attention head, and normalizes the score matrix by using the Softmax function.

[0077] Furthermore, the normalized score matrix is ​​subjected to tensor product operation with the value vector of the corresponding modality, and the aggregated multimodal features are superimposed across layers using the residual connection operator.

[0078] Specifically, the system reconstructs features through the last linear mapping layer containing 128 neurons, ultimately generating a global semantic representation vector with a dimension of 128. This vector carries the tension, logical rigor, and deep contextual features of the candidate's non-verbal expression in the form of a structured binary floating-point array, and is sent to the competency assessment module as the input benchmark for the final decision.

[0079] Furthermore, the mapping of variable-length physical representation vectors to a high-dimensional hypersphere semantic space includes: extracting the local topological structure of the audio physical representation using a manifold learning algorithm, and introducing an orthogonal constraint operator during cross-modal projection to ensure that the spatial relationship of the dot product of the projected vectors remains monotonically consistent with the Euclidean distance of the original temporal envelope; when the variance of the cosine distance distribution after projection exceeds a preset threshold of 0.05, this threshold (0.05) is a critical variance determined based on the convergence consistency experiment of the feature distribution within the projection space. When using Laplacian feature mapping for manifold alignment, the system judges the mapping quality by monitoring the dispersion of feature points in the low-dimensional space; after extensive backtesting with heterogeneous corpora, it was found that when the projection variance exceeds 0.05, it means that two audio segments with similar energy curves have significant manifold distortion or feature divergence in semantic topology (i.e., mapping distortion), and at this time, the original linear mapping can no longer maintain the local neighborhood relationship of the data. By setting this precision threshold, the spatial folding calibration operator can be precisely triggered to perform geometric compensation for nonlinear distortion, ensuring the forced alignment accuracy of multimodal data at the semantic level. The system performs spatial folding calibration; when aligning heterogeneous data, it uses Laplacian feature mapping to ensure that the corresponding feature vectors of two audio segments with similar energy curves remain adjacent after being projected into the semantic space, preventing semantic space distortion caused by linear projection and ensuring that subtle differences in pitch can accurately reflect the linear increase in emotional intensity; it solves the "manifold distortion" problem in the process of mapping physical features to semantic space; it reduces the semantic similarity measurement error between physical modalities and text modalities by 12.5%, ensuring the structural stability of high-dimensional fusion vectors.

[0080] By introducing high-dimensional hypersphere semantic space mapping and mutual information entropy conflict verification, the problem of inconsistent dimensions and semantic conflicts of heterogeneous data is overcome. It can intelligently identify conflict scenarios such as "saying the opposite of what is meant" and dynamically adjust the modal contribution, so that the generated global semantic representation vector has a higher degree of consistency with the expressive intent.

[0081] The preceding cross-modal fusion module obtains a 128-dimensional global semantic representation vector containing the full-dimensional expressive features of candidates, and extracts the recruitment interview scenario labels associated with the original multimodal dataset as the initial condition for triggering the deep quantitative analysis logic.

[0082] Reference Figure 2 The flowchart illustrates the process of performing in-depth quantitative analysis and outputting a digital analysis report. Further, the system performs content semantic quantitative analysis. Based on the recruitment and interview scenario tags, the system retrieves the corresponding job competency knowledge graph from the distributed graph database. This graph contains 300 core skill nodes and their associated logical hierarchical relationships. The global semantic representation vector is projected onto the graph space using a semantic mapping model based on graph convolutional neural networks.

[0083] Specifically, the semantic mapping model consists of two graph convolutional layers. The first layer contains 128 hidden neurons, and the second layer contains 64 neurons. By calculating the Euclidean distance between the input vector and the professional skills nodes and project experience nodes in the graph, the coverage density of the core semantics in the candidate's answer is identified. The cosine similarity algorithm is used to compare the candidate's answer logic chain with the preset structured logic template. The content dimension score is obtained by quantitative calculation and is 85.5 points.

[0084] Furthermore, interactive sentiment cross-analysis is performed, and the system constructs a verification model based on a two-dimensional sentiment mapping matrix. The model architecture consists of two parallel convolutional neural network subnetworks, each containing three convolutional layers and one global pooling layer, which are used to process the acoustic polarity features in the physical representation and the semantic sentiment components in the text, respectively.

[0085] Specifically, the validation model aligns the emotional labels such as tension and confidence mapped from the physical modality with the positive and negative polarities displayed in the text semantics in a temporal sequence, and performs consistency judgment in a two-dimensional matrix. When it identifies a conflicting attribute segment where the speech frequency fluctuation shows high stress characteristics while the text literal shows high confidence, it calls the sentiment shift correction operator and dynamically allocates penalty weights by calculating the mutual information values ​​of the two modalities. The corrected comprehensive sentiment quantification value is 0.72.

[0086] Furthermore, routine assessments of expressive traits are conducted. The system extracts normalized speech rate features, pause ratios, and frequency fluctuation amplitude components from the global semantic representation vector. It also retrieves a benchmark threshold dataset for recruitment scenarios, which specifies that the standard interview speech rate is 150 to 180 characters per minute, and the proportion of logical pauses should be less than 10%.

[0087] Specifically, the construction process of the benchmark threshold dataset for recruitment scenarios includes: First, collecting massive amounts of high-scoring interview audio and video data and using ASR technology for text alignment to establish an original feature library; Second, using speech activity detection operators and word throughput algorithms, performing statistical distribution analysis on high-quality communication samples to calculate a high-frequency normal distribution range of 150 to 180 characters / minute; Subsequently, using information entropy theory to quantify semantic coherence, and determining the negative correlation critical point (10%) between the proportion of logical pauses and comprehension efficiency through comparative experiments; Finally, standardizing and encapsulating the extracted speech rate features and pause ratio indicators to form a reference dataset with industry benchmark attributes, providing a precise technical benchmark dimension for subsequent multimodal decision-making.

[0088] Specifically, the algorithm logic calculates the deviation vector between the measured parameters and the benchmark threshold by performing subtraction operations, and uses a multilayer perceptron regression model to calculate the fluency index. This model contains a hidden layer with 32 neurons, and the parameters are locked by training with the Adam optimizer. The final output is that the candidate's interaction rhythm frequency is 1.2 times per second, and the fluency score of 0.88 reflects the language organization ability.

[0089] Furthermore, the system matches the allocation coefficients of each dimension in the weight configuration table based on the recruitment interview scenario tags. Specifically, the content semantic weight is set to 0.5, the interaction emotion weight is set to 0.25, and the expression characteristic weight is set to 0.25. By performing a weighted summation operator on the quantitative results of the three dimensions, the system calculates the candidate's total scenario evaluation score to be 82.25.

[0090] Specifically, the system utilizes the dispersion characteristics of the total evaluation score and the scores of each dimension to perform a retrieval operation based on the K-nearest neighbor algorithm in a pre-set digital suggestion library. It identifies the lowest-scoring fluency index as a breakthrough point for improvement and matches it with targeted suggestion text, such as suggesting shortening sentence length to reduce speaking speed pressure.

[0091] Furthermore, the scenario-based results output module encapsulates the aforementioned quantitative indicators, key node indexes, and improvement strategies in a structured manner, generating a digital analysis report that includes radar chart distribution data and JSON-formatted suggested terms.

[0092] Specifically, the assessment report is sent to the recruiter's management terminal via a high-speed Ethernet interface, and all analysis data is simultaneously written to the persistent partition of the talent pool database, completing the entire technical loop from the original interview interaction to the generation of digital competency profiles.

[0093] By constructing a two-dimensional emotion matrix and a multi-dimensional evaluation weight model, an objective transformation from emotional interaction to digital quantitative analysis was achieved. Combined with an expert database matching mechanism, targeted improvement strategies were generated, enhancing the interpretability and practical value of the analysis results and providing a standardized closed-loop evaluation method for various dialogue scenarios.

[0094] This embodiment constructs a complete technical closed loop from the perception of original interaction signals to the generation of multi-dimensional quantitative evaluation reports by deeply coupling a deep residual reduction network, a bidirectional Transformer architecture, and a collaborative attention fusion mechanism. The system utilizes mutual information entropy to quantify the consistency probability between physical features and textual semantics, and combines an expert knowledge base and a dynamic weight optimization mechanism to solve the semantic gaps and feature distortions that are easily generated in traditional single-modal analysis in complex interaction environments, ensuring a high degree of consistency between the competency evaluation results in terms of physical authenticity and logical rigor.

[0095] This system constructs a full-link digital evaluation architecture for AI recruitment and interview scenarios by deeply coupling the acquisition module, feature extraction module, semantic analysis module, and parsing report module. The parsing report module ultimately outputs an evaluation report that includes a radar distribution map, key node index, and digital improvement strategies. This achieves automated and standardized flow from underlying perception signals to high-value talent profiles, solving the technical problems of large subjective biases and loss of non-verbal features in traditional interviews.

[0096] Example 2: The following detailed description of the specific implementation methods of the present invention, combined with more specific recruitment interview scenarios and other feasible scenarios, clearly presents the actual application process and technical effects of the system: The system deployment and scenario configuration are as follows: The system described in this invention is deployed on the enterprise human resources AI interview platform, and it is connected with the existing interview system and talent pool system through API interface to achieve data interoperability. HR selects the "Recruitment Interview" scenario in the scenario configuration module, and the system automatically loads the preset evaluation dimensions and weights: job matching degree (30%), expression ability (25%), professional quality (15%), learning potential (15%), stability (10%), and stress resistance tone characteristics (5%). At the same time, the JD (job description) of the target position is imported, and the system uses NLP technology to extract core skill keywords (such as "Python programming", "project management", "cross-departmental collaboration") and job requirements (such as "more than 3 years of experience" and "strong stress resistance"), builds a job-specific thesaurus and evaluation standards, and marks the weight coefficient of core keywords (such as "Python programming" has a higher weight than "office software operation").

[0097] Furthermore, multimodal data acquisition includes: During the AI ​​interview process, the system collects candidate voice data through the high-fidelity noise-canceling microphone built into the interview device, focusing on capturing acoustic features such as speech rate, pitch changes, pause duration, tone intensity, and stress position. The sampling rate is set to 16kHz to ensure speech clarity. At the same time, ASR technology is used to transcribe the speech into text data in real time, marking the timestamp of each sentence and linking it one by one with the original voice data. HR can add notes (such as candidate interview status tags, special Q&A instructions) through the text input module. All data is synchronously transmitted to the data storage module and linked to information such as candidate ID, job ID, and interview stage (self-introduction, professional Q&A, stress test) to ensure data traceability.

[0098] Furthermore, data preprocessing includes: the preprocessing module performs targeted processing on the collected data to eliminate interference from invalid information. At the text data level, the jieba word segmentation algorithm is used to segment the transcribed text, and part-of-speech tagging is performed using the HMM model. Stop words and filler words are removed based on a general stop word library and a recruitment-specific stop word library (such as "interviewer," "um," "this"), and grammatical errors are corrected (e.g., "I was in charge of a project, the project was about XX" is corrected to "I was in charge of a project about XX"). At the same time, the occurrence position and frequency of core keywords for the job are marked. At the speech data level, an adaptive noise cancellation algorithm is used to eliminate background noise and equipment interference in the interview environment. Features such as speech rate (normal range 150-180 words / minute), pitch fluctuation amplitude, pause duration, and tone intensity are extracted using STFT. Cases of single pauses exceeding 3 seconds, speech stuttering, and abnormal speech rate (below 120 words / minute or above 220 words / minute) are identified and marked to ensure the effectiveness of speech features.

[0099] Furthermore, contextual semantic modeling and analysis includes: The contextual semantic modeling module initiates its work by performing bidirectional contextual feature learning on preprocessed text data based on the BERT model. A multi-layered attention mechanism is used to focus on core keywords related to the job position and inter-sentence relationships. For example, when a candidate answers "I have been responsible for projects and led the team to achieve the goals," the preceding question "Please introduce your project management experience" clarifies that the "project" refers to the project management experience required for the position, rather than ordinary execution projects. A session state cache is constructed using the GPT model, linking multiple rounds of question-and-answer logic to determine whether the answer deviates from the core of the question (e.g., if the interviewer asks about professional skills and the candidate only describes their work experience, it is marked as "irrelevant"). Simultaneously, the speech acoustic feature vector is integrated into the contextual semantic vector to achieve bimodal semantic fusion.

[0100] The “session state cache” is used to store historical interaction semantic feature vectors for a predetermined number of rounds and to update the semantic vectors generated in the new round of interaction; it includes at least: a data structure for storing the historical semantic vector sequence, and an update rule for applying decay or replacement to the historical vectors according to the time order; by calculating the similarity between the current semantic vector and the aggregated vector in the cache, the degree of deviation of the current round of interaction from the historical context can be quantified.

[0101] The multi-dimensional semantic analysis module simultaneously conducts in-depth analysis: In the content semantic dimension, it calculates the keyword matching score for the job posting using the TF-IDF algorithm, analyzes the logical structure of the answers (e.g., whether it follows the logical chain of "project role - responsibilities - problems encountered - solutions - final result"), and identifies the authenticity and relevance of the candidate's project experience; in the sentiment dimension, it judges whether the candidate is nervous (speaking too fast, speaking in a flustered manner, with drastic tone fluctuations) or confident (speaking at a moderate pace, speaking firmly, with a stable tone) through cross-validation of voice pitch fluctuations, tone intensity, and text sentiment; in the expression feature dimension, it quantifies the fluency of expression (number of pauses, frequency of stuttering) and language organization ability (semantic coherence, concise expression), and, combined with the changes in voice sentiment during the stress test, assesses the stress resistance tone feature score.

[0102] Furthermore, the results generation and output include: a scenario-based results output module calculates the candidate's scores across various dimensions based on preset weighting coefficients, generating a visual competency assessment report. The report clearly marks the candidate's strengths (e.g., "high relevance to project management keywords, clear logical answers, firm tone, and strong confidence"), weaknesses (e.g., "speaking too fast, frequent pauses, insufficient fluency, and slightly flustered tone when answering stress questions"), and the total job fit score (out of 100). It also generates candidate rankings (comparing multiple candidates for the same position). The report displays the scores for each dimension in radar chart format, allowing HR to view the original dialogue text, audio clips, and key feature markers. Data is pushed to the HR terminal in real time and automatically stored in the talent pool, linked to candidate interview records for easy review and tracing.

[0103] Furthermore, model optimization includes: HR reviewing the assessment report and providing feedback for correction. For example, if they believe the system's score for "expression ability" is too low, they manually adjust the score and add a note that "although the candidate paused, the logic of the expression was clear, and the expression ability was qualified." The model optimization module collects this feedback, marks it as a correction sample, and, combined with the bimodal data from this interview, adjusts the weight of the correlation algorithm between speech features (number of pauses, frequency of stuttering) and expression ability score, reducing the negative impact of the number of pauses. At the same time, as interview data for the same position accumulates, the job keyword library and assessment criteria are continuously optimized (such as adding recognition rules for expressions related to "cross-departmental collaboration"). K-fold cross-validation is used to eliminate model bias and improve the accuracy of subsequent interview analysis for the same position.

[0104] In customer feedback scenarios, the system can collect text and voice data of conversations between customer service representatives and customers through a similar process. It extracts core customer needs from the semantic dimension (such as "product quality issues," "slow service response," and "cumbersome refund process"), assesses customer satisfaction and dissatisfaction levels from the sentiment dimension, and analyzes the friendliness and responsiveness of customer service representatives from the expression characteristic dimension. This generates a summary report of customer feedback, problem classification statistics, and service improvement suggestions. In teaching interaction scenarios, the system collects text and voice data of classroom conversations between teachers and students. It assesses students' knowledge mastery from the semantic dimension (knowledge point matching score), and analyzes students' interaction enthusiasm and confidence in their answers from the sentiment and expression characteristic dimensions. This provides teachers with assessments of individual student learning status and suggestions for adjusting overall class teaching. Its core implementation logic is consistent with the recruitment scenario mentioned above, with only differences in the scenario-based analysis dimensions, weights, and evaluation standards. The system can quickly switch between scenarios through the scenario configuration module.

[0105] Furthermore, the iterative update of historical semantic vectors using the weight decay operator includes: constructing a memory weight matrix based on an exponential decay function; performing nonlinear reduction on semantic features spanning more than three interaction rounds; and determining a forced topic switch when the cosine offset exceeds a preset threshold of 0.7, triggering a cache reset operator, and assigning a high-priority initial weight to the new topic. For example, in a customer review scenario, when a customer suddenly jumps from feedback on "product quality" to "logistics speed," the system recognizes the sudden change in semantic deviation rate, automatically reduces the cache weight of the quality dimension, and quickly constructs a logical vector for the logistics dimension to prevent old context from interfering with the recognition of new intents. This solves the problems of semantic drift and invalid memory interference in long-range dialogues, shortens the average delay of topic switch point recognition, and improves the sensitivity of multi-turn dialogue logic monitoring.

[0106] The discrimination threshold (0.7) is determined based on the geometric distribution characteristics of the angle between vectors in the high-dimensional feature space. In the cosine similarity quantification system, 0.7 corresponds to an angle deviation of approximately 45 degrees between feature vectors. According to the clustering statistics of the pre-training corpus of the large model, when the cosine offset exceeds this threshold, it means that the current real-time features and the baseline features have a significant directional deviation in the semantic dimension (i.e., the core semantics have drifted). Experimental verification shows that setting the threshold to 0.7 can effectively identify abnormal expressions or logical breaks that deviate from the standard recruitment scenario, triggering subsequent compensation operators or correction mechanisms.

[0107] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for semantic analysis of AI data based on dialogue interaction, characterized in that, include: Acquire audio timing signals and text data during the interaction process; extract the physical representation parameters of the audio timing signals and map them into structured text with timestamps; simultaneously convert the text data into a standard encoding format and associate it with scene labels; establish a coupling mapping relationship between audio timing signals and text data on the time axis to form a multimodal raw dataset. Based on the aforementioned multimodal raw dataset, natural language algorithms are used to perform serialization annotation and redundant information filtering on the text data to extract core semantic features; An adaptive noise suppression algorithm is used to purify the audio timing signal, and a physical representation vector containing rhythm features, frequency fluctuations and energy intensity is extracted through time-frequency transformation. A hybrid model architecture integrating a bidirectional Transformer encoder and decoder is constructed. The encoder extracts bidirectional contextual features of the text for semantic disambiguation, and the decoder's session state cache dynamically constructs a logical coherence representation vector for multi-turn interactions. The physical representation vector is projected onto the semantic space and fused with the logical coherence representation vector in dimension alignment to generate a global semantic representation vector. Perform in-depth quantitative analysis and generate reports.

2. The AI ​​data semantic analysis method based on dialogue interaction according to claim 1, characterized in that, The establishment of the coupling mapping relationship between audio timing signals and text data on the time axis includes: The hardware abstraction layer of the integrated acquisition module synchronously acquires the clock reference of the audio data stream and the interrupt signal of the text input system, and uses a concurrent processing mechanism to segment the audio data into time-domain acoustic segments; during the extraction of physical representation parameters, the alignment between the text representation and the original waveform envelope is calculated based on the dynamic time warping algorithm to calibrate the timing deviation caused by data encoding delay. For manually entered non-real-time text, semantic similarity retrieval is used to locate its logical mapping point in the interactive semantic stream. Then, the sliding window technique is used to perform secondary clustering mapping between the non-real-time text and the physical representation vector of the corresponding time period to construct a structured original dataset with an asynchronous compensation mechanism.

3. The AI ​​data semantic analysis method based on dialogue interaction according to claim 1, characterized in that, The extraction of core semantic features includes: The corresponding domain knowledge base is retrieved based on scene tags, and domain weight labeling is performed on the word segmentation results; statistical models are used for syntax annotation and dependency parsing trees are constructed to identify and retain restrictive modifiers with semantic guidance. When filtering redundant information, the energy density distribution and temporal length of candidate filter items in the original dataset are calculated to retain non-lexical physical features with semantic emphasis function, and a probabilistic prediction model is used to correct semantically ambiguous segments, thereby achieving high-fidelity semantic compression of interactive text.

4. The AI ​​data semantic analysis method based on dialogue interaction according to claim 1, characterized in that, The process of purifying and extracting physical representation vectors from audio time-series signals includes: The deep residual reduction network is used to identify non-stationary interference substrates in the interactive environment and perform nonlinear suppression to extract the purified physical envelope signal. When performing time-frequency transformation, the step size of the analysis window is dynamically adjusted according to the speech rate parameter of the interactive subject to capture the starting position and pause interval of the signal. A multi-scale attention mapping model is constructed to perform normalized alignment between the audio frequency fluctuation curve and the energy distribution feature, generating a dynamic feature operator that represents the interactive rhythm. The dynamic feature operator is then used to perform confidence weighting on the speech rate feature to eliminate feature distortion caused by modal overlap.

5. The AI ​​data semantic analysis method based on dialogue interaction according to claim 1, characterized in that, The hybrid model architecture and dynamically constructed logical coherence representation vectors include: A cross-attention operator is introduced into the encoder to inject feature vectors from multiple rounds of historical interactions into the representation process of the current text, thereby achieving semantic completion of indicator pronouns and omitted components. A multi-dimensional session state cache is constructed using the hidden layer state of the decoder, and the historical semantic vector is iteratively updated using a weight decay operator to identify logical transition signals of interactive topics. The logical deviation rate of the current interaction relative to the historical context is quantified by calculating the cosine offset between the current vector and the aggregated vector of the session state cache, and the logical deviation rate is incorporated into the logical coherence representation vector as a constraint term.

6. The AI ​​data semantic analysis method based on dialogue interaction according to claim 1, characterized in that, The generation of the global semantic representation vector includes: By using a cross-modal projection matrix based on a gating mechanism, the variable-length physical representation vector is mapped to a high-dimensional space with the same dimension as the logical coherence representation vector, so as to eliminate the dimensional differences between the physical modality and the text modality. During the fusion process, mutual information entropy is used to calculate the consistency probability between physical representation and text semantics. When a conflict is found between the emotional intensity expressed by physical features and the literal semantics of the text, a dynamic weighting operator is used to adjust the feature contribution of different modalities, and a nonlinear fusion is performed through a collaborative attention mechanism to generate a global semantic representation vector containing non-linguistic expression details and deep contextual features.

7. The AI ​​data semantic analysis method based on dialogue interaction according to claim 1, characterized in that, The process of performing in-depth quantitative analysis and outputting a report includes: Content semantic quantitative analysis: Based on scene tags, knowledge graphs are invoked to quantify the distribution of core semantics, and text similarity algorithms are used to determine the completeness of the logical structure, extract explicit needs and implicit intentions, and generate content dimension scores; Interactive sentiment cross-analysis: Construct a two-dimensional sentiment mapping matrix, perform temporal distribution consistency verification between the sentiment labels mapped from physical features and the semantic polarity of the text; perform weighted calibration on attribute conflict fragments through sentiment offset correction operator, and output a comprehensive sentiment quantification value; Routine assessment of expressive traits: compare speech rate, pause ratio, and frequency fluctuation amplitude with the scene baseline threshold to calculate the fluency index and interaction rhythm frequency; Based on the scene label matching weight coefficient, the results of each dimension are weighted and summed to obtain the total scene evaluation score. Based on the scoring deviation, the suggestion library is retrieved to match improvement measures, and an evaluation report containing a multi-dimensional distribution map, key node index and digital improvement strategy is generated.

8. An AI data semantic analysis system based on dialogue interaction, characterized in that, include: Acquisition module: Acquires audio time-series signals and text data during the interaction process; extracts the physical representation parameters of the audio time-series signals and maps them into structured text with timestamps; synchronously converts the text data into a standard encoding format and associates it with scene tags; establishes a coupling mapping relationship between audio time-series signals and text data on the time axis; and forms a multimodal raw dataset. Feature extraction module: Based on the multimodal raw dataset, natural language algorithms are used to perform serialization annotation and redundant information filtering on the text data to extract core semantic features; An adaptive noise suppression algorithm is used to purify the audio timing signal, and a physical representation vector containing rhythm features, frequency fluctuations and energy intensity is extracted through time-frequency transformation. Semantic analysis module: Constructs a hybrid model architecture that integrates bidirectional Transformer encoder and decoder. The encoder extracts bidirectional contextual features of the text for semantic disambiguation. The decoder uses session state caching to dynamically construct logical coherence representation vectors for multi-turn interactions. The physical representation vectors are projected onto the semantic space and fused with the logical coherence representation vectors in dimension alignment to generate a global semantic representation vector. Analysis Report Module: Performs in-depth quantitative analysis and outputs a report.

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