121 results about "Verbal learning" patented technology

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.

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.

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.

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 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.

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 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.

We provide the system. [Solution] A learning plan generation means that generates a language learning plan based on user input, A voice input receiving means that receives voice input corresponding to a conversation scenario provided in accordance with the learning plan, An analysis means that analyzes the aforementioned voice input in real time and generates feedback regarding pronunciation and grammar, A means for presenting the aforementioned feedback to the user, An adjustment means for adjusting the learning plan based on the user's learning progress, A system that includes this.

This application belongs to the field of data processing technology, specifically involving an integrated traceability method for language learning and practice results across multiple platforms. The method includes: acquiring learning behavior data from multiple platforms; standardizing and converting the multi-platform learning behavior data and writing a unified event serial number to generate a unified learning record; writing user identifiers, timestamps, record summary values, and previous record summary values ​​into the unified learning record to generate a chained record sequence; storing the chained record sequence in a storage network, and generating a restricted access copy and an integrity verification copy when the sharing request is verified; performing behavior fragment coverage checks and supplementary collection based on the integrity verification copy to generate a complete set of learning behaviors; and integrating the complete set of learning behaviors across platforms to generate a learning trajectory archive. This application can balance record reliability, sharing controllability, and trajectory integrity.

The invention relates to the technical field of natural language processing and artificial intelligence application, and discloses a multilingual spelling error correction method based on big language modeling, which comprises the following steps: generating multilingual training data through a disturbance strategy, and supervising and finely tuning a big language model; zero sample error detection is realized based on lexical-level probability deviation; and a detection result and a generative error correction result are integrated, and only consistent errors are replaced. According to the method, the excessive error correction tendency of the generative model is effectively inhibited, the error correction performance of low-resource languages such as Vietnamese and Indonesia is improved, and meanwhile, the principle of'minimum change 'is kept. The method does not need manual data annotation, has good mobility and cross-language adaptability, and can be widely applied to scenes such as text cleaning, language learning and machine translation.

The invention provides a language memory card system with a pronunciation error correction function, and relates to the technical field of language training. The language memory card system with the pronunciation error correction function comprises a user portrait and configuration system, a core learning and interaction system and a background management and optimization system which are bidirectionally linked to realize refined adaptation of dialects and crowd pronunciation and memory-pronunciation collaborative reinforcement. The user portrait and configuration system is used for collecting user basic information, completing a pronunciation baseline test, generating personalized configuration parameters and outputting the personalized configuration parameters to the core learning and interaction system; and the core learning and interaction system receives personalized configuration parameters. The system has the dialect and crowd refined adaptation capability, the pronunciation error correction precision is high, memory and pronunciation collaborative enhancement is achieved, personalized adjustment and dynamic optimization are supported, data safety is guaranteed, and the language learning efficiency and normalization are remarkably improved.

The present invention relates to a method for providing a learner-personalized language learning service and, more specifically, to a method for providing a learner-personalized language learning service, which is performed by a server and provides a Korean learning service to a foreign learner. The method comprises the steps of: preparing, by the server, an evaluation corpus including a learning corpus for allowing a learner to learn Korean and an error corpus in which an error made by the learner is expressed; receiving learner information input using a learner terminal; setting a Korean language ability level of the learner on the basis of the learner information; and allowing the learner to learn by using the evaluation corpus according to the set Korean language ability level of the learner. The present invention provides language learning data according to an individual language learning ability level of a learner and a subject desired by the learner, and enables personalized language learning to be performed for each learner, thereby improving learning efficiency and satisfaction.

The invention provides a processing system and method for learning languages in real time by means of ideographic marks of a plurality of oracle or images in combination with surface phonetic marks of a large language model. And new language learning experience is provided for the user by utilizing an artificial intelligence automatic content generation and communication computing power technology. According to the invention, multiple languages are learned through composition and association of the ideographic marks. And if people want to understand the representative meaning, the described scene or meaning can be understood from the combination or the combination. According to the method, one or more large language models are allowed to be used, and ideographic marks including characters, pictures, videos and the like are learned in real time. Users of different native languages are allowed to learn online or on the spot through operation of the system; a card or a virtual image game is performed. An oracle bone image-text knowledge base can also be constructed by using the image on the front side of the playing card and one or a plurality of prompt instructions on the back side; and a model for processing the voice marks is trained into a large language model of the ideographic marks to crack unrecognized oracle bone inscriptions.

The invention discloses a two-language learning and interdisciplinary cognitive construction training system and method, which adopts a 7 + 1 core model of'structured task training of 7 days per week and one closed-loop detection ', is a cognitive and language double-track development system, and comprises a teacher module and a student module, a forced English learning cognition closed loop is formed through a seven-day structured task chain in a systematic input stage and one-time multi-dimensional detection in a closed loop detection stage, the system combines language acquisition with contents of science, technology, engineering, mathematics and the like, and realizes deep fusion of language acquisition and interdisciplinary knowledge construction. By creating a mandatory and double-track integration system of'seven-day structured task training and one-time closed-loop detection ', language learning is sublimated into interdisciplinary cognitive practice for exploring the world through English, the problems of splitting and thinning of an existing tool are fundamentally solved, and an efficient path is provided for cultivating compound talents adapting to the future.

The invention discloses a hearing aid and spoken language partner training integration method and system based on hearing loss and compensation, and belongs to the crossing field of hearing aid and artificial intelligence language learning. The method is cooperatively executed by a Bluetooth headset and a mobile terminal application program, and comprises the following steps: establishing a personalized hearing loss curve of a user; the equipment has an intelligent hearing aid mode and a spoken language partner training mode. And in the hearing aid mode, real-time compensation playing is carried out on the environment sound based on the curve. In the partner training mode, three partner training processes of real-time, preset scene and word generation scene are provided, and adaptation processing is performed on the generated partner training audio based on the same hearing curve, so that key teaching phonemes are ensured to be clear and audible. The core innovation lies in that the system dynamically adjusts the compensation parameter of the hearing loss curve by analyzing the pronunciation accuracy of the user in partner training, and realizes collaborative optimization and personalized improvement of the hearing aid effect and the spoken language ability.

The invention relates to a computer-implemented method (100) for providing a personalized virtual assistant for controlling a vehicle function, using a pre-trained language learning model that has been trained to provide text data generated at an output level from content data provided at an input level, comprising at least the following steps: generating (102) text data from content data of a plurality of documents using the at least one pre-trained language learning model; embedding (104) the plurality of documents in the text data; creating (106) at least one vector database from the text data with the embedded documents; generating (108) conversational text data from at least one user input using the at least one pre-trained language learning model; embedding (110) the at least one user input in the conversational text data;Generating (112) response text data as a response of the personalized virtual assistant to the user's at least one input based on the conversation text data with the at least one embedded user input by means of the at least one pre-trained language learning model and the at least one vector database; and providing (128) a control signal displaying the response text data for controlling an output device.

A computer-implemented method for facilitating learning of a language through gamification is disclosed. The method includes receiving, via a communication device, a request for learning the language from a user device and retrieving, from a storage device, a character comprising at least one stroke. A processing device determines a musical sound associated with the at least one stroke and causes the character to be displayed on the user device. The method further includes receiving at least one user stroke generated based on a gesture made by the user, analyzing the at least one user stroke in relation to the corresponding stroke of the character, and modifying the musical sound based on the analysis to generate a modified musical sound. The modified musical sound is transmitted to the user device to provide interactive, music-based feedback during language learning.

With the state of the art, it is not possible to utilize rearrangement problems having information origins based on the essence of learning processes, namely that learning is to understand concepts and to understand connections between the concepts. The present invention makes such utilization possible for both educational learning and language learning.　This learning assistance system uses a rearrangement problem of which an information origin is customized and is characterized by a step of analyzing and acquiring a book, audio, video content, and other relevant information selected by a learner by using a natural language processing technique or a machine learning algorithm in accordance with a profile or a preference and needs of the learner, a step of creating, on the basis of the acquired information, the customized rearrangement problem in a language desired by the learner, a step of converting the created problem into a signal, digital data, or other formats and transmitting the conversion result to a terminal used by the learner by using an optical fiber cable, a wireless communication, the Internet, or other communication means, a step in which the learner answers by using the rearrangement problem, a step of determining whether the answer is correct or wrong, a step of conjecturing the cause of an incorrect answer to the rearrangement problem and creating appropriate advice by using a natural language processing technique or a machine learning algorithm in consideration of one or both of the level of the contents or a language level of the learner, and the function which allows the learner to select the steps and a desired language at a point designated by the learner within each step, the function being capable of processing the selections by using a natural language processing technique, a machine learning algorithm, or other suitable technical means.

Effectively support language learning based on pattern practice. [Solution] The language learning device of the present invention is a language learning device that provides learning content to a user terminal, and has an example sentence storage means that breaks down example sentences, which are learning content, into grammatical components and stores them, an example sentence display means that breaks down the example sentences into their components and displays them on the user terminal, and a randomization means that randomly switches at least one of the components displayed on the user terminal to another component.

The invention discloses an intelligent reading feedback evaluation system fusing multi-modal perception, and relates to the technical field of language learning. The system comprises an audio input module, a voice processing module, a bilingual content storage module, an ear return feedback module and an audio output module, wherein the audio input module collects reading voices of a user; the voice processing module converts the voices into texts and extracts rhythm parameters; the bilingual content storage module stores bilingual corresponding data; and after the user reads a sentence, the system immediately plays the corresponding target language audio. In addition, multi-user role allocation and permission alternation, local low-delay networking, learning content progressive unlocking and personalized ear return audio track management can be realized, language learning immersion and family participation degree are improved, and privacy and learning effect are guaranteed.