147 results about "Verbal learning" patented technology

The invention discloses a language learning assistance application system based on speech recognition, and belongs to the technical field of learning assistance. The system comprises an intelligent voice acquisition and enhancement module for realizing acquisition and preprocessing of high-quality voice signals in a complex environment; the multi-modal feature extraction module dynamically extracts and fuses multi-dimensional learning feature information, completes feature analysis and boundary segmentation of a phoneme level, and is responsible for feature normalization and optimization; the deep speech recognition module is used for realizing high-precision speech recognition based on the normalized features; the intelligent evaluation engine module is used for realizing real-time and multi-dimensional evaluation on the language performance of the learner; the personalized learning management module integrates the evaluation results for dynamically planning an optimal learning path for the learner, manages the learning progress, and provides learning effect prediction and intervention suggestions at the same time; and the learning interaction experience module is responsible for visual presentation of learning feedback, multi-modal man-machine interaction design and user interface optimization.

The invention relates to the technical field of artificial intelligence and language learning, in particular to a multi-modal language learning auxiliary system and method based on artificial intelligence, and the system comprises a multi-modal input module, a cross-modal feature fusion module, a dynamic adaptive learning module and an interactive feedback generation module. Wherein the multi-mode input module is used for receiving original text data, original image / video data and original voice data; the cross-modal feature fusion module is used for extracting a visual feature vector and a semantic coding vector and generating a cross-modal joint feature vector; the dynamic adaptive learning module is used for generating dynamic scene parameters; and the interactive feedback generation module is used for outputting a multi-mode feedback data packet through a prompt learning engine. According to the method, by fusing multi-modal feature alignment and dynamic context parameter modeling, linkage generation of grammar, culture and pronunciation feedback is achieved, and the context understanding ability and interaction feedback precision in the language learning process are improved.

A method and system for creating an enhanced domain-specific language learning model is disclosed. In some embodiments, the method includes training a domain language model using domain-specific data. The method includes receiving input corpus for one or more downstream tasks. The method then includes using the domain language model using the input corpus to generate a first set of embeddings, and using a pre-trained large language model (LLM) using the input corpus to generate a second set of embedding. The method further includes combining the first and second sets of embeddings to form a combined set of embeddings and perform the one or more downstream tasks using the combined set of embeddings.

The invention relates to the technical field of language learning, and discloses an English auxiliary pronunciation training method and system based on multi-task learning, and the method comprises the following steps: constructing a multi-task learning framework which comprises at least two subtask models related to pronunciation, comprising the steps of pronunciation accuracy scoring, pronunciation error type positioning and intonation fluency analysis. A voice signal, a lip movement video and a text content of a user are collected through a multi-modal data input module; and performing joint coding on the multi-modal data by using the shared feature extraction layer to generate fused acoustics. According to the English auxiliary pronunciation training method and system based on multi-task learning, the pronunciation training path of the user can be adaptively adjusted, the requirements of different users can be met, the English pronunciation level can be more effectively improved, and through strategies such as multi-modal data input, shared feature extraction, inter-task attention mechanism and adaptive training path adjustment, the English pronunciation training efficiency can be improved. Accurate evaluation and personalized training of English pronunciation are realized.

The invention belongs to the technical field of online learning, and discloses a real-time interactive online Chinese language learning platform. Comprising the steps of constructing a virtual culture interaction scene; in the dialogue interaction process, continuous dialogue fragments are collected in real time; performing multi-level language bias error analysis on the continuous dialogue segments, identifying language errors, making corresponding language correction suggestions according to the language errors, and feeding back the language correction suggestions to the user through a progressive feedback mechanism; collecting multi-modal data of a user, carrying out nonlinear feature mapping, generating a multi-dimensional state feature vector, and dynamically evaluating a cognitive load and a learning state; the cognitive load and the learning state are fused, interaction regulation and control strategy reasoning is carried out, and the interaction mode and the interaction difficulty in dialogue interaction are dynamically optimized according to an interaction regulation and control strategy; according to the method, accurate control and dynamic regulation and control of the whole Chinese language learning process of the user are realized, personalized online Chinese learning experience is provided, and comprehensive improvement of the language ability of the user is promoted.

The invention discloses a multi-modal emotion calculation enabling adaptive interactive language learning system and a matching method thereof, and belongs to the technical field of artificial intelligence, and the system comprises a multi-modal emotion real-time perception and quantification module MHAE-Net, a personalized adaptive dialogue and intervention strategy module ADIS-RL, and a virtual dialogue partner interactive interface module EC-NLG. The multi-modal emotion real-time perception and quantification module MHAE-Net is composed of a voice signal acquisition and processing unit, a voice emotion analysis unit, a text emotion analysis unit, a facial expression emotion analysis unit, a physiological signal emotion analysis unit and a multi-modal emotion fusion and decision-making unit. The interactive strategy is dynamically adjusted, the oral anxiety of the language is effectively relieved, the oral confidence is improved, the system adopts the multi-mode emotion calculation technology, the accuracy and robustness of emotion recognition are improved, the targeted interactive strategy is designed for different emotion states, and personalized emotion support and language practice are provided for learners.

The invention provides a language learning dynamic resource configuration and interaction system based on an internet platform, relates to the technical field of data processing systems, and provides a double-layer cascade diagnosis mechanism. Through a first calculation module, a state anomaly score is calculated based on the stability of user interaction behaviors instead of simple correctness, so that beneficial struggling and harmful fatigue are accurately distinguished; when the score exceeds a threshold value, a second calculation module is activated, a specific learning fragment is analyzed in combination with eye movement trajectory data, and a cognitive deviation value for quantifying specific cognitive impairment is calculated; on one hand, through accurate state recognition, wrong intervention during deep thinking of the user is avoided, and the learning heart stream is effectively protected; and on the other hand, through accurate cognitive attribution, the system can provide targeted accurate assistance, the tutoring efficiency and the learning effect are improved, and intelligent teaching upgrading is realized.

Various embodiments disclosed relate to a method of producing an analysis document using generative artificial intelligence, the method comprising: maintaining a database containing multiple language learning models and a collection of prompts; generating a patent prosecution tool user interface integrated with a generative artificial intelligence program; receiving a user input comprising a matter associated with at least one matter dataset, where the matter dataset includes a plurality of patent documents associated with the matter; selecting a language learning model from the database; retrieving a plurality of patent documents for display on the patent prosecution tool user interface; loading at least one of the plurality of patent documents into the artificial intelligence program; displaying more than one prompt from the collection of prompts; initiating a script in the generative artificial intelligence program based on a user selected prompt and the at least one patent document; analyzing the patent document according to the script to produce a preliminary analysis; producing the analysis document according to the preliminary analysis; saving the analysis document.

The invention provides an auxiliary language training system based on artificial intelligence, belongs to the technical field of artificial intelligence, and realizes paradigm innovation of language learning through deep fusion of a quantum-classical hybrid architecture and a multi-modal neuroscience technology. A quantum semantic analysis engine and a cross-language parallel corpus generation module break through culture barriers, and a dynamic knowledge graph is fused with anthropology features and a taboo word weight quantification model, so that the cross-culture semantic accuracy is improved by 78%; a multi-modal synchronous compensation algorithm is combined with brain oxygen metabolism real-time monitoring, cross-modal delay of a VR / AR scene is compressed to be within 70 ms, a neural morphological pronunciation correction module and a 4D bionic tongue model construct a pronunciation-auditory feedback closed loop, the TTS phoneme fidelity of a small language reaches 97.9%, and 39% of language interference is reduced compared with a traditional model. A logic traceability network driven by a quantum entanglement dialogue module can analyze 12 layers of grammar nesting errors, and a dynamic correction path is generated in cooperation with a pulse neural dictionary.

The invention provides an AI-powered speech, language therapy, and language learning system that utilizes Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) to deliver personalized, real-time therapy and learning for individuals with speech disorders or those seeking to improve language proficiency. The system analyzes user speech, language comprehension, and facial expressions, providing immediate feedback on pronunciation, fluency, articulation, and sentence structure. A GAN-generated avatar interacts with the user, mimicking human expressions and offering dynamic, engaging sessions. The platform adapts exercises based on user performance using personalized algorithms to ensure continuous progress. Additionally, it securely stores user data in compliance with privacy regulations, making it accessible through web and mobile platforms. This invention improves upon existing speech therapy and language learning solutions by integrating real-time visual and auditory feedback with AI-driven personalization, offering a more immersive and effective experience.

The invention discloses an intelligent language learning method based on multi-modal fusion, and belongs to the field of intelligent language learning. Comprising the following steps: acquiring a voice input signal, lip-shaped video data and text input data of a user; performing framing windowing processing on the voice input signal to obtain a voice feature vector; extracting lip shape motion feature vectors from the lip shape video data; extracting a text semantic feature vector from the text input data; inputting the voice feature vector, the lip motion feature vector and the text semantic feature vector into a multi-modal alignment module to obtain an aligned multi-modal feature representation; on the basis of the aligned multi-modal feature representation, personalized language learning content is dynamically generated through a condition generation model; and generating multi-modal feedback information according to the response of the user to the language learning content. A multi-modal attention alignment mechanism is adopted, the learning efficiency can be improved, and the memory retention rate can be improved.

A tailored interactive language learning system that teaches an individualized set of vocabulary words to users through interactive avatars and stories. The interaction is modeled through probabilistic rules in a semantic network and neural network having objects and relations. Dialog and narration is generated dynamically based on the state of the interactive story model using phrasal rewrite rules and neural network implementiung a four-valued logic system in which truth values of the objects and relations are encoded as true, false, defined, and undefined in a single memory array.

The invention belongs to the technical field of language learning, and discloses a language training method and device, equipment and a medium, and the method comprises the steps: constructing a candidate training unit pool; calculating a push priority score of each training unit in the candidate training unit pool; sorting all the training units in the candidate training unit pool from large to small according to the push priority scores, and selecting a preset number of training units; aiming at each selected training unit, adopting an interference strategy which corresponds to the training unit and contains grammar errors and / or cultural conflicts, generating training questions corresponding to the training unit based on a large language model, and forming a training question set by all the training questions; and performing language training according to the training question set. And the language anti-interference capability, the cross-culture sensitivity and the deep grammar grammar of the user can be effectively improved.

The invention discloses an English teaching method based on somatosensory interaction, and relates to the field of interactive teaching, and the method comprises the steps: collecting limb behaviors and voice data of a user, and generating a teaching task instruction based on a teaching theme selected by the user; matching actions and voices of a user with a target grammar structure and a vocabulary set by constructing a mapping library of limb behaviors and English communication intentions; analyzing the limb movement data through a space-time diagram convolutional network, recognizing movement features, and generating a voice intention identifier through voice recognition and semantic analysis; comparing the consistency of the limb behavior and the voice intention, and if a conflict exists, generating a correction instruction; and dynamically adjusting the task difficulty according to the historical performance of the user, and increasing visual and voice interference until all teaching objectives are completed. The method has the advantages that by collecting the limb movement and voice data of the user in real time and combining the dynamic task and the intelligent error correction system, the language learning experience with high interactivity is achieved, and the learning effect and the user participation degree are effectively improved.

A cloud-integrated, AI-powered smart speaker for language learning, which includes the following: A microphone array for speech capture; ◯ an on-device AI module with a Neural Processing Unit (NPU) or Tensor Processing Unit (TPU) for real-time speech analysis; • Wi-Fi and 5G connectivity for secure data synchronization with cloud servers; ◯ a cloud-based AI engine that uses serverless computing services to trigger AI models for advanced language assessment and adaptive learning; ◯ a cloud storage system with relational databases for structured data, such as user profiles and progress metrics, and object storage for unstructured data, such as audio recordings, secured with AES-256 encryption and RBAC for data protection; ◯ a true-to-life speaker for AI-generated speech output; ◯ a screen (OLED / E-Ink) for text corrections and real-time lesson guidance; ◯ Touch and gesture sensors for user interaction; ◯ A battery module with USB-C charging function for portability.

The invention relates to the technical field of speech recognition, and particularly discloses a language autonomous learning system based on big data speech recognition, and the system comprises an acquisition module which carries out the grouping of users according to regions, marks the users in the same group as target users, and obtains a speech signal when the target users carry out the language learning; the processing module is used for performing framing processing on the voice signals to obtain voice frames, extracting voice features of the voice frames based on a voice recognition technology, and determining sub-features based on a deep learning model; the optimization module is used for generating coordinate points, clustering the coordinate points to obtain clusters, performing descending sorting on the clusters according to the density, and determining a target cluster based on the sorting; and the analysis module is used for acquiring the target character, acquiring an analysis result of the target character based on the target cluster, and not sending an error prompt when the analysis result is dialect abnormity. According to the invention, frequent sending of error prompt information caused by dialects of language learners can be avoided.

The invention discloses a language learning evaluation method based on a deep learning algorithm, and relates to the technical field of language processing, and the method comprises the steps: receiving a voice signal and original text content, constructing a noisy voice sample through an adversarial disturbance generator, inputting a pre-trained voice recognition model, and outputting a voice recognition text and an anti-noise confidence score; constructing a context semantic graph in the semantic analysis path, generating an edge attention weight, calculating a semantic coherence index, and matching a predefined grammar rule base in the activated grammar analysis path to generate a grammar error tag; and dynamically generating feedback content based on the multi-dimensional evaluation matrix, and updating a preset corresponding threshold value and a rule base weight according to the adoption of the user on the feedback content. According to the method, collaborative quantitative analysis of pronunciation, semantics and grammar problems is realized by constructing a multi-dimensional evaluation matrix of an anti-noise confidence score, a semantic coherence index and a grammar error tag.

The invention provides an AI-based multi-user interactive language learning system and method, and relates to the technical field of interactive language learning. Real-time structured analysis of a multi-user mixed voice stream is realized through a voiceprint separation technology, a grammar error density spectrum is constructed to quantify user grammar error distribution characteristics, and a culture conflict intensity field is established; cross-cultural dialogue conflict intensity is dynamically calibrated, a semantic transition trajectory is generated, a logic fault evolution law is accurately captured, and a traditional single-dimensional static learning mode is broken through; a language adaptation index is dynamically generated on the basis of a weight prediction model, collaborative modulation of a grammar reconstruction task package, a culture tuning script and a semantic bridging task is achieved, culture conflict mediation deeply conforms to real-time conflict intensity, and dialogue fault features are accurately matched through semantic logic repair; through a three-channel task distribution mechanism of directional insertion, multicast broadcast and random allocation, efficient collaboration of personalized grammar training, immersive culture drill and distributed logic repair is ensured.

The invention discloses a multifunctional intelligent Chinese language learning system. The system comprises the following modules; a data acquisition and arrangement module; an analysis and learning path planning module; a learning interaction and real-time feedback module; a knowledge consolidation and expansion module; a learning achievement evaluation and excitation module; the invention also discloses a multifunctional intelligent Chinese language learning machine which comprises the following modules: a hardware module; a software module; the functions are matched with one another, the Chinese character learning effect of a learner can be remarkably improved, understanding and mastering of Chinese character culture connotation are deepened, in addition, the pressure sensing device in the display screen can detect the writing strength and assist in analyzing the learning state, and due to the collaborative design of software and hardware, complete, efficient and personalized learning experience is provided for the learner.

The disclosed embodiments describe a method, system, and computer-readable medium for generating a training dataset for training a model in the field of natural language processing involving receiving a set of input samples and performing a rephrasing operation to produce new versions of the set of input samples, where the new versions preserve semantic equivalence as the set of input samples but have different phrasing. A dataset of generated versions of the input samples is generated using a generative Language Learning Model (LLM), all entity references present in the generated versions of the input samples are labeled, and the generated versions of the input samples and their corresponding labeled versions to form an expanded labeled dataset are aggregated.

An intelligent classroom analysis and feedback tool for language learning, including, • a data input interface (101) configured to receive student-generated speech input consisting of written and oral contributions. • a preprocessing and normalization engine (102) for standardizing and preparing the speech inputs for analysis. • a natural language processing (NLP) pipeline (103) to perform linguistic, grammatical, pronunciation, lexical, discourse and sentiment analysis of the inputs. • a student clustering and profiling module (104) that can be used to classify students based on a common model of such error and performance patterns. • Teacher-oriented analytics dashboard (105), customized to visualize trends in language performance at the class and individual levels. • a teaching recommendation engine (106) that recommends educational interventions in order for weW patterns. • an engagement and sentiment tracking subsystem (107) adapted to monitor emotional and behavioral cues from language use; and • an LMS integration layer (8) in operational communication with the system to embed the system in educational platforms and provide feedback with an existing course content and gradebooks of the student.

Various embodiments described herein relate to systems and methods for constructing a classification model using data associated with a facility. In this regard, unlabeled data associated with the facility is retrieved. Based on an application of a first algorithm on the unlabeled data, parameters in the unlabeled data are determined. Using the parameters, machine learning algorithms identify topics and clusters in the unlabeled data. Also, a prioritized list of keywords is generated for each of the topics. Then, the prioritized list of keywords is input to a language learning model. The language learning model outputs a keyword from the list of keywords. If the keyword meets a predefined threshold, a cluster of the clusters is labeled using the keyword. Using first set of labeled clusters, the classification model is trained. The trained classification model provides recommendations related to the facility which are also renderable on a user interface as well.

A location-based game may include language learning features to help a player learn a foreign language. To generate these features, the game may generate language information by allowing players to play language gathering games. For example, a virtual character can use a native language of a player to instruct the player to identify an object in an environment. The player may then identify the object in the environment. The player may also speak the name of the identified object. The name of the object, a recording of the player speaking the name, and pixel values representing the object may be associated together. By gathering this information across different languages, a database can be formed that includes language information for many objects. This language information may then be used to generate language learning features.

The invention relates to the technical field of language learning, and discloses an English pronunciation practice auxiliary device, which comprises a storage plate, the surface of the storage plate is provided with a radio assembly and a camera assembly, the upper surface of the storage plate is provided with an entertainment area, and the entertainment area is internally provided with an entertainment mechanism which is used for simulating a cartoon situation. The children's attention is attracted through dynamic actions, the entertainment mechanism comprises a vibration assembly and a moving assembly, and the vibration assembly is used for achieving ejector rod vibration through electromagnetic driving and generating tactile feedback of different frequencies and intensities. By integrating the functions of audio processing, dynamic visual analysis and entertainment interaction, pronunciation data and dynamic facial features of a practicer can be collected in real time, accurate pronunciation problem recognition is achieved through a scientific algorithm, the learning interest and pronunciation accuracy of the practicer are improved through multi-sensory interaction, and the system is particularly suitable for the language learning requirements of children.

A system for implementing interactive language learning includes a microphone, an audio output unit, a data storage unit, a display unit, and a controller. The controller generates an audio signal related to a question stored in the data storage unit, and controls the audio output unit to output the audio signal. In response to receipt of a speech signal, the controller outputs a text answer based on the speech signal. The controller determines whether the text answer corresponds with one of a plurality of predetermined answers associated with the question, generates one of an affirmative response and a guidance response based on the determination, transforms the one of the affirmative response and the guidance response into a form of speech, and controls the audio output unit to output the one of the affirmative response and the guidance response as a new audio signal.

Systems and methods for AI-assisted user training and flow query including receiving training including tokenizing the training data with a language learning model into one or more vectors, including a processing vector, and storing the one or more vectors. The systems and methods may receive a request from a user and generate a proficiency score by inputting the request into an adaptive response engine comprising the proficiency vector and proficiency logic and assign the proficiency score to the user. The systems and methods may generate a response associated with the request based on the proficiency score and programed query response logic and output the response.

The invention relates to the technical field of artificial intelligence and gamification education, and discloses an AI personalized virtual accompanying dialogue system based on a language learning level, and the system comprises a file obtaining module which is used for obtaining a historical dialogue file of a user before the dialogue of the user starts; the plot setting module is used for performing five-dimensional quantization processing on the current dialogue statement of the user according to the historical five-dimensional vector, and setting a dialogue plot key of the user based on a current CEFR interval, a historical key structure and a historical hit result in the current five-dimensional vector; the frequency collection module is used for outputting the demonstration springback text to the user and collecting the current springback frequency of the demonstration springback text on the basis of the historical springback frequency; the task completion module is used for completing a conversation completion task of the user; and the virtual accompanying module is used for realizing AI personalized virtual accompanying dialogue processing of the user. According to the method, immersive experience and efficient grammar training can be achieved at the same time.

1. Name of the product in this design: Learning Machine (Chinese Language). 2. Purpose of this design: For Chinese language learning. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The invention relates to the technical field of children language rehabilitation training, and provides a children language rehabilitation training system based on voice and mouth shape recognition, which comprises a mouth shape simulation frame, a voice and mouth shape recognition module, a voice and mouth shape recognition module and a voice and mouth shape recognition module, an external camera is used for tracking and simulating the lips and the face of the training object in a model animation mode; and the tracking and capturing module is used for collecting face data of the training object and feeding back the face data to the model of the simulation mouth shape frame so as to fit the face of the training object and improve the training conformity. According to the invention, specific facial features and mouth shapes of each child can be individually fitted, customized rehabilitation training is provided, the mouth shapes of the children can be better fitted, more accurate pronunciation mouth shapes can be guided to be made, meanwhile, guide animations and roles which attract the attention of the children can be generated, the participation degree of the children is improved, the rehabilitation training is more interactive and interesting, and the interestingness of the children is improved. And the language learning interest and effect of the children can be improved.