346 results about "Pinyin" patented technology

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses a speech recognition error correction method, which comprises the following steps of: training and generating a target field Chinese character text understanding model by collecting dialogue Chinese character texts of a target field; collecting pinyin data, and training to generate a pinyin understanding model; receiving to-be-detected voice data and converting the to-be-detected voice data into to-be-detected Chinese character texts and pinyin texts Inputting the Chinese character text and the pinyin text to corresponding understanding models to generate semantic features and pinyin features; combining the semantic features and the pinyin features to form joint features; inputting the joint features into a speech recognition error detection model, generating error probability distribution of each word, and outputting an error tag; and correcting the Chinese character text through a template error correction module based on the error tag. According to the method, more comprehensive feature representation is formed by combining semantic features and pinyin features, and the accuracy of speech recognition error detection is improved; and a template error correction module is utilized to effectively correct identification errors.

The invention relates to the technical field of speech synthesis, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses a speech generation method, device, equipment and medium. And inputting a text speech language model to generate an intermediate code in combination with a speech code extracted based on a codebook generation mode, extracting a speaker feature vector in the prompt speech, decoding the intermediate code and the speaker feature vector by a generative adversarial decoder, and outputting a target speech. According to the method, fine-grained text representation is established by fusing character semantics and pinyin pronunciation information, and voice cloning is completed through unified voice language modeling and an adversarial generation mechanism in combination with codebook-driven acoustic coding and speaker personality characteristics, so that the naturalness, similarity and pronunciation accuracy of generated voices are improved.

The invention discloses a traffic domain service voice adaptive generation method and system based on a thinking chain fine-tuning large model, and the method comprises the steps: converting an input voice signal into a high-dimensional voice feature signal through a voice encoder, and generating a text output signal according to the high-dimensional voice feature signal through a text decoder and a pinyin decoder; according to the invention, a variational de-quantization joint data enhancement mechanism, a dual-channel multi-mode discriminator architecture and a layered composite loss function are adopted to carry out high-fidelity emotional speech generation and robustness synthesis in a complex noise environment, and semantic-driven dynamic rhythm optimization and professional term precise pronunciation are synchronously supported; in an application scene in the traffic field, a multi-task speech recognition method can be adopted to realize efficient linkage of character-level recognition, audio-to-pinyin conversion and sentence-level accent classification modules, so that complex accent, more noise and traffic term polyphone challenges are effectively handled, and the method is suitable for being widely popularized and used.

The invention relates to the technical field of network security and data processing, and discloses a violation short message recognition method and system based on deep semantic understanding, and the method comprises the steps: firstly cleaning an original short message, generating a mixed embedding vector through characters, sub-words and pinyin, and carrying out the recognition of the violation short message; then processing through a double-layer detection engine, wherein the first layer utilizes rules and a lightweight model for rapid preliminary screening; in the second layer, for suspected samples, a double-tower fusion neural network architecture is adopted, local and global features are combined, fusion is carried out through a gating unit, and a large language model is input to carry out deep semantic reasoning. The system executes strategies such as interception or flow limiting according to the risk score, and realizes model iteration through a dynamic knowledge base and incremental learning. According to the method, the resource consumption and the detection precision are balanced through the layered architecture, the antagonistic variants are effectively identified by utilizing multi-dimensional feature fusion, and the method has the adaptive evolution capability for a novel violation mode.

The invention relates to a speech recognition model training method, a speech recognition method, a speech recognition device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining a mixed text sample, and obtaining a voice sample and a tagged text sample based on the mixed text sample; performing feature extraction and coding on the voice sample to obtain a coding matrix; respectively inputting the coding matrix into a text recognition decoder and a conversion recognition decoder in a to-be-trained speech recognition model to obtain a text recognition result and a text conversion recognition result, and calculating a first model loss based on the text recognition result and the mixed text sample; calculating a second model loss based on the text conversion recognition result and the tagged text sample; and training a to-be-trained speech recognition model based on the first model loss and the second model loss to obtain a speech recognition model. The speech recognition model trained by the method can improve the recognition accuracy of the Chinese pinyin mixed speech.

The invention discloses a short message identification method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring a short message to be identified; a pre-trained short message classifier is adopted to perform feature extraction on the to-be-recognized short message to obtain multi-dimensional features, the multi-dimensional features at least comprise one of font features, pinyin features and semantic features, the font features are used for representing structures of characters in the to-be-recognized short message, the pinyin features are used for representing pronunciation of the characters in the recognized short message, and the semantic features are used for representing semantic features of the characters in the recognized short message; the semantic features are used for representing the meaning of the to-be-recognized short message; fusing the multi-dimensional features to obtain fused features; and identifying the fusion features by adopting a short message classifier to obtain the type of the short message to be identified. The technical problem that the short message recognition accuracy is low is solved.

The present invention relates to the technical field of adversarial text processing, and disclosed is a Chinese adversarial sample recovery method based on an HMM and a denoising auto-encoder. The method comprises: acquiring an adversarial text, and carrying out symbol cleaning on the adversarial text; converting pinyin strings in the adversarial text into Chinese character sequences on the basis of a hidden Markov model, and splicing the Chinese character sequences into Chinese character strings; performing denoising on the Chinese characters and the Chinese character strings in the adversarial text by means of a denoising auto-encoder, so as to obtain a candidate word sequence; decoding the candidate word sequence by means of a confidence-similarity decoder, so as to obtain a corrected text; and processing the corrected text by means of bidirectional machine translation to generate a recovered Chinese character text. The present invention can achieve efficient recovery of Chinese adversarial samples.

The invention discloses a large model-based finance and tax question and answer sensitive information identification method and system, and the method comprises the steps: obtaining finance and tax question data, and carrying out the processing of the obtained finance and tax question data; key prohibited word detection is carried out on the processed finance and tax question data through an established Chinese pinyin sensitive word Trie tree; when it is judged that no key forbidden word exists in the finance and taxation question data, detecting violation sensitive words through violation semantics in a trained finance and taxation large model; and when no violation sensitive word is detected in the output of the large finance and taxation model, outputting the finance and taxation question data to a normal question and answer system. According to the method, finance and taxation violation oriented questions and answers are screened through the finance and taxation large model obtained through training, the Chinese pinyin sensitive word Trie tree is constructed, the detection effect is improved by training the semantic comprehension ability of the large model, and sensitive text recognition is achieved.

The utility model discloses a word spelling teaching aid for Chinese learning, and relates to a Chinese learning tool, in particular to a Chinese polyphonic polysemy word spelling learning tool. The card splicing box comprises a storage box (1), a card splicing box (2) and cards (3), each group of cards comprises a certain polyphonic polysemy, a character card (31) respectively printed with polyphonic polysemy pinyin, a pronunciation card (32) respectively printed with pronunciation different from that of the character card, and a word group card (33) corresponding to the pronunciation card; the storage box is arranged above the card splicing box; a cavity for storing the card (3) is arranged in the storage box (1); and a group of character cards, pronunciation cards and word group cards which are arranged side by side can be accommodated in the card splicing box. The device is a polyphone spelling practice device, and solves the problems that an existing similar device is complex in structure, depends on electric power in use, and is inconvenient to popularize and use.

The invention relates to the technical field of power system dispatching and maintenance management, discloses a word vector optimization equipment name intelligent matching method based on an equipment knowledge graph, and aims to solve the problem of low matching precision caused by nonstandard naming, OCR recognition errors and semantic ambiguity of power distribution network equipment names. The method comprises the following steps: constructing an equipment knowledge graph containing a triple structure; corpora are generated based on the atlas, a Word2Vec model is adopted to train equipment word vectors, and semantic features are captured; calculating the character similarity through the editing distance and the pinyin similarity; using word vector cosine similarity to match semantic association; and carrying out weighted fusion on the character and semantic similarity, and dynamically judging a matching result. According to the method, through multi-modal similarity fusion and knowledge reasoning, the matching accuracy can be improved, naming regionalization, abbreviation and topological association problems are effectively processed, scheduling instruction analysis, maintenance data standardization and cross-system data management are supported, and reliable technical support is provided for intelligent management of a power grid.

The invention relates to the technical field of Chinese braille text intelligent conversion, and discloses a deep learning-based Chinese braille text intelligent conversion system, which comprises an input module, an image processing module, a data set construction and preprocessing module, a model training and optimizing module, an output processing module and a text conversion module, handwritten braille alphabets are recognized through mobile phone photographing, and the image processing module comprises image inclination correction and sharpening and braille alphabet image segmentation. The method comprises the following steps of: identifying by using a two-party model, leaving a part with confidence higher than a certain value, identifying the rest part by using a one-party model, leaving a part with confidence higher than a certain value, arranging the two parts according to a pixel sequence to form a format suitable for LLM input, and generating corresponding text representation; the problem that Chinese braille symbols are not in one-to-one correspondence with initial consonants and final consonants is avoided, conversion from Chinese pinyin to Chinese texts is achieved in combination with a large language model, and the error-tolerant rate of large language model translation to original texts is high.

The invention relates to the technical field of input methods, in particular to a full-hybrid input method for Chinese and English input. During initial full-mixed input, after a letter is input into a first key of each word, a background algorithm passes through full spelling, binary spelling and pinyin and two-key shape code letter input channels; after the first key of each word inputs a note isolation, a background algorithm passes through a simple spelling input channel at a time; after the English comma is input into the first key of each word, a background algorithm passes through a full-shape code input channel at a time; after a decimal point is input into the first key of each word, a background algorithm passes through an English input channel once; for single input of various input methods, after needed characters and words are submitted, the input is automatically returned to the initial fully-mixed input; and in a full-spelling and binary-spelling five-sound display mode, inputting a note insulation, inputting a background algorithm through a pinyin and two-key shape code alphabet input channel at a time, inputting pinyin and two-key shape code alphabet at a time, and automatically returning to initial full-mixed input after required words are submitted.

A voice disease recognition method based on MFCC and CNN comprises a patient audio acquisition module, an audio data preprocessing module, an MFCC feature extraction module, a classification model construction module and a patient voice disease category recognition module which are connected in sequence. The audio data preprocessing module uses a VAD endpoint detection algorithm to cut and unify recorded effective segments, and the MFCC feature extraction module performs operations such as weighting, windowing, transformation and filtering on the cut audio to obtain an exclusive MFCC feature map as a training object of a downstream classification task; the classification model construction module feeds a feature map containing pathological information to the voice disease recognition module. According to the method, the voice information of the vowel pronunciation of the Chinese pinyin of the patient is utilized to realize the classification and identification of four voice diseases (spasmodic dysphonia, vocal cord paralysis, vocal cord nodules and vocal cord polyps) with similar pathological expressions.

The invention relates to a brain-computer interface interaction system and method for inputting Chinese characters based on handwriting imagination, and belongs to the technical field of brain-computer interfaces. The system comprises a user interaction module which is used for recording and storing information such as names, genders and ages of subjects and selecting and setting experimental paradigms of handwriting imagination tasks; according to the electroencephalogram data acquisition module, a subject is guided by a handwriting imagination experiment normal form to carry out a corresponding handwriting imagination task, and offline electroencephalogram signals are acquired for multiple times; the model training module is used for constructing an off-line classification model through the handwritten imaginary electroencephalogram signals collected for multiple times; the on-line identification module is used for classifying the signals through the model and carrying out on-line classification identification; and the Chinese character input method module is connected with a character API (Application Program Interface) in an open input method and generates Chinese characters according to the classified strokes or pinyin. The invention provides a novel brain-computer interaction mode based on noninvasive brain imaging for disabled people who lose a handwriting function.

An air conditioner control method and system based on accent speech recognition comprises the following steps that a language adjusting command is set, and a plurality of language databases are constructed according to the language adjusting command and various dialects; wherein the language adjustment command comprises a wake-up command and a control command; acquiring an audio of a wake-up command input by a user, and uploading the audio to a cloud; converting the audio into a first pinyin, finding out a target language database from a language database according to the first pinyin, and loading a language control configuration file associated with dialects in the target language database into the air conditioner. According to the method, the audio is directly converted into the first pinyin, and more details can be exposed on the pinyin level, so that matching can be performed more accurately during matching, and the recognition efficiency of dialects is improved. Therefore, the user can interact with the equipment in a more natural and familiar manner, so that the operation convenience and satisfaction of the user are greatly improved.

The invention provides a method and equipment for analyzing adverse event information of a clinical test. The method comprises the following steps: acquiring audio information of a target object; outputting a corresponding pinyin sequence based on the audio information by using a pre-trained speech recognition model; performing text prediction on the pinyin sequence to obtain an output text; in response to the fact that the knowledge-enhanced large language model is utilized to determine that an adverse event exists in the output text, key information in the output text is extracted; performing named entity identification based on the key information to obtain name information, time information and degree information in the key information; and constructing adverse event information based on the name information, the time information and the degree information. By implementing the method and the device, the analysis processing can be automatically performed according to the description of the target object to obtain the corresponding adverse event information, compared with manual processing, the speed is higher, the method and the device are not interfered by clinical environments or other factors, and analysis tasks can be efficiently completed in batches.

The invention belongs to the field of speech recognition, and provides an AI speech recognition method and a tablet personal computer, and the method comprises the steps: extracting the time domain information of an input one-dimensional audio signal, and converting the time domain information into a two-dimensional Mel spectrogram according to the Mel scale; the two-dimensional Mel spectrogram is input into a convolutional neural network model, the convolutional neural network model obtains a three-dimensional feature map by using binarization weight and through sliding window operation and convolution operation during feature extraction, a remodeling layer remodels the three-dimensional feature map into two-dimensional features, a full connection layer classifies the two-dimensional features, and the two-dimensional features are classified into a three-dimensional feature map; mapping to a probability space of a preset Chinese pinyin factor tag, and converting an output value of the full connection layer into probability distribution of each phoneme by the Softmax layer; inputting the output probability distribution of each phoneme into a connection time sequence classification decoder, and converting the optimal phoneme sequence into a Chinese text; according to the invention, the problems of high calculation complexity and high hardware resource demand in traditional speech recognition can be solved.

The invention discloses an emotion category determination method and device, equipment, a storage medium and a product. The method comprises the following steps: acquiring audio data; extracting an MFCC feature of the audio data and a first feature of a pinyin syllable; converting the audio data into a text, and determining an intonation embedding feature of each word in the text, and a word embedding feature, a position embedding feature and a segment embedding feature of the text; fusing the MFCC feature, the first feature, the intonation embedding feature, the character embedding feature, the position embedding feature and the segment embedding feature to obtain a second feature; and inputting the second feature into an emotion recognition model, and determining a target emotion category corresponding to the second feature according to relationship information between a preset feature and a preset emotion category in the emotion recognition model. Various features such as voice features, text features, intonation features and tone features based on human auditory characteristics are fused, and the accuracy of determining the emotion category of the audio data is improved.

According to the method, a Chinese dialect English speech corpus covering multiple regions and multiple dimensional variables is constructed, the influence of Chinese dialect phoneme migration on speech parameter differences is deeply analyzed, and a Fujissaki model, a pinyin phoneme theory and a hidden Markov model (HMM) are creatively fused; high-precision automatic speech recognition (ASR), high-naturalness speech synthesis (TTS) and intelligent pronunciation correction of Chinese dialect English are realized. The method is suitable for the application fields of domestic English education, cross-regional multi-language interaction, intelligent voice assistants and the like, and effectively solves the problems of low recognition rate, separation of synthetic voice and actual accent and the like when a traditional system processes Chinese dialect English.

The invention provides a pinyin decoding input method, an embedded system and a storage medium. The method comprises the following steps: constructing a hierarchical code table index database; obtaining a pinyin character string input by a user, and determining a first index of the pinyin character string in the initial index layer according to the initial of the pinyin character string; calculating a Hash value of the pinyin character string by using a Hash algorithm, and obtaining a second index corresponding to the Hash value in a Hash table; searching in the pinyin data block according to the second index to obtain a pointer corresponding to the pinyin character string; and matching in the word data layer by using the pointer to obtain corresponding word data. According to the method, the memory pressure of the embedded system is reduced, the three-layer index structure of the hierarchical code table index database is utilized, and the optimized hash search algorithm is adopted to accelerate data access, so that high-precision matching and quick positioning are realized in an embedded environment.

The invention discloses a text-to-instruction system based on fuzzy pinyin matching and parameter robust analysis. The text-to-instruction system comprises an input preprocessing module, a text-to-instruction conversion module and a text-to-instruction conversion module, the fuzzy pinyin matching module is used for uniformly transferring the standardized text and a pre-stored instruction template into a silent pinyin sequence, and constructing a weighted editing distance matrix based on a preset pinyin character replacement cost; the parameter alignment search module is used for executing beam search on the weighted editing distance matrix and outputting a plurality of candidate matching paths with the minimum cost and a corresponding parameter fragment set; the parameter robust distribution module is used for performing legality and consistency checking on parameters in the candidate matching paths, completing parameter standardization and performing error correction in combination with a parameter candidate dictionary; the instruction output module is used for encoding the template identifier and the parameter key value pair obtained through robust distribution into a structured instruction and outputting the structured instruction; according to the method, text-to-instruction conversion can be completed in real time.

A storage format of Chinese language (“Readable Hanyu Expression” or “RHE”) and the related processing methods and systems. Unlike current Chinese processing methods which directly code Chinese characters into fonts for display, RHE takes an indirect approach by storing Chinese language in the RHE storage format that can be mapped to several display forms including simplified and traditional Chinese characters, Hanyu Pinyin, etc. In the RHE storage format, each Chinese word is stored as an RHE storage element having the format (Syllable+Tone)n+Mark, where n is the number of syllables (Chinese characters) in the word, Syllable represents the pronunciation (without the tone) of the character, Tone represents the tone of the pronunciation, and Mark is a value that differentiates different words having the same pronunciations and tones. Various mapping tables are used to map RHE storage elements to standard Chinese character codes (such as Unicode) and Pinyin expressions.

The application discloses a character input method and device, electronic equipment and a storage medium, and belongs to the technical field of electronic equipment. The method comprises the following steps: receiving a first input for inputting a word group pinyin in an input method keyboard; in response to the first input, displaying at least one word group unit corresponding to the word group pinyin, each word group unit comprising at least one candidate word; in response to a second input for selecting a target word group unit from the at least one word group unit, displaying the target word group unit in a text input area corresponding to the input method keyboard.

The invention discloses a commodity order information intelligent identification method based on natural language processing, and belongs to the technical field of natural language processing. Aiming at the problem of low recognition accuracy of unstructured text order information, the invention provides an intelligent recognition method. The method comprises the following steps: receiving an unstructured text; preprocessing, including conversion from traditional Chinese characters to simplified Chinese characters, space removal and keyword filtering; commodity names are extracted by adopting accurate matching and pinyin fuzzy matching; extracting numbers and quantity identifiers; analyzing the number of each commodity, including processing the expression of each character, the expression of a direct number, the expression of a negative number and the expression of 0; and outputting the structured order information. In addition, the method can also process agency orders and serial number orders, identifies commodity name variants through a sliding window technology and Pinyin similarity calculation, improves order processing efficiency, enhances the capability of processing complex expressions, reduces labor cost, and improves user experience.

The invention relates to the technical field of data processing, in particular to a data information storage method and system for an online Chinese language learning platform. The method comprises the following steps: obtaining platform user interaction data, and carrying out pinyin input track analysis and Chinese character component structure coding to obtain a pinyin alignment input frame; extracting a pinyin-Chinese character mapping relation in the pinyin alignment input frame, and performing Chinese character structure path reconstruction to obtain a user input word structure path set; learning content data are called, multi-modal content node embedding is carried out, and a content linkage fragment cluster is obtained; constructing a single session learning topology track according to the content linkage fragment cluster, and performing bidirectional topology sorting to obtain a learning track topological graph; and establishing a multi-dimensional primary key index and behavior triggering mechanism for the learning track topological graph to obtain a dynamic index distribution block, and transmitting the dynamic index distribution block to an online Chinese language learning platform. According to the invention, the stability and expansibility of the learning platform are obviously improved.

1. Name of the Design Product: Language, Math and English Enlightenment Learning Machine. 2. Use of the Design Product: Mainly applicable to preschool children and primary school students for learning pinyin, Chinese characters, English, mathematics, and training logical thinking, etc. It consists of a key panel, a display screen, a key film, paper cards (which can be freely replaced infinitely), a middle frame, a bottom shell, speakers, an electronic circuit, a battery, a camera, a recording microphone, etc. Users can freely replace the learning cards. 3. Design Key Points of the Design Product: Lies in the front view. 4. Picture or Photo that Best Illustrates the Design Key Points: Front view.

The invention provides a pinyin broadcasting method. The pinyin broadcasting method comprises the following steps: acquiring a pinyin text to be broadcasted; extracting feature values corresponding to the pinyin components in the pinyin text to be broadcasted, and synthesizing the feature value groups corresponding to the pinyin components into sample feature values according to a preset sequence; performing generation processing on the target features through a trained voice generation model to obtain a target voice waveform of the pinyin text to be broadcasted; and performing pinyin broadcasting on the pinyin text to be broadcasted based on the target voice waveform. According to the invention, the problem that follow-up addition is not easy to maintain due to the fact that audio needs to be stored in advance, stored in a cloud environment and downloaded to a local terminal for playing before broadcasting in the existing broadcasting method and the audio files stored in the local terminal are too many is solved.

The invention discloses a speech recognition method and device, equipment and a storage medium. The method comprises the following steps: acquiring speech feature information of at least one speech chunk in speech to be recognized; time sequence classification processing is carried out on the speech feature information of a first chunk located at the initial position in the at least one chunk to obtain a first probability matrix of the first chunk, the first probability matrix comprises at least one probability vector, and the probability vector comprises probability values of multiple pinyin with tones; determining the phonetic alphabets with the probability values meeting a preset condition in the first probability matrix as target phonetic alphabets; and displaying a target single character corresponding to the target pinyin in a preset mapping table, wherein the preset mapping table comprises a plurality of pinyin with tones and a single character corresponding to each pinyin with tones. According to the embodiment of the invention, the first word on the screen is quickly determined, so that the perception of the user on the first word on the screen is improved, and the user experience of voice recognition is improved.

A storage format of Chinese language (“Readable Hanyu Expression” or “RHE”) and the related processing methods and systems. Unlike current Chinese processing methods which directly code Chinese characters into fonts for display, RHE takes an indirect approach by storing Chinese language in the RHE storage format that can be mapped to several display forms including simplified and traditional Chinese characters, Hanyu Pinyin, etc. In the RHE storage format, each Chinese word is stored as an RHE storage element having the format (Syllable+Tone)n+Mark, where n is the number of syllables (Chinese characters) in the word, Syllable represents the pronunciation (without the tone) of the character, Tone represents the tone of the pronunciation, and Mark is a value that differentiates different words having the same pronunciations and tones. Various mapping tables are used to map RHE storage elements to standard Chinese character codes (such as Unicode) and Pinyin expressions.

The application provides a sentence processing method and device and electronic equipment. After voice data is converted into a text form of a sentence through automatic speech recognition, the sentence is further split into multiple processing parts, and an entity term included in the sentence is fuzzily queried according to pinyin of the multiple processing parts to obtain an entity term corresponding to the processing part in an entity index. When the text of the processing part is different from the entity term, the processing part in the sentence is replaced by the entity term, so that the text of the entity term in the sentence is correct, and the command of the sentence can be accurately determined through natural language understanding in the subsequent process, the command indicated by the user is finally accurately executed, and the user experience of the electronic equipment is improved.