17 results about "Language identification" patented technology

The invention discloses an Internet of Things process engine automatic arrangement method and system based on a large model. The method comprises the steps of obtaining a process demand described by a natural language of a user; the method comprises the following steps: analyzing a natural language of a user through a first-layer large model, and identifying related equipment, variables, conditions and action information; information identified by the first-layer large model serves as input of a second-layer large model, and the second-layer large model matches actual corresponding equipment and variable information under the user permission based on the user token, the equipment and the variable information; transmitting actual corresponding equipment, variables, conditions and action information under the user permission to a third-layer large model; and the third-layer large model matches the corresponding flow nodes according to the received information, the matched flow nodes are connected in series through a connecting line, and a structured flow JSON is generated. According to the method, the fuzzy natural language intention is gradually converted into an accurate and executable structured process through the three-layer large model, and the accuracy of an output result of each link is improved.

A method for treating a language disorder in a patient includes receiving therapist input specifying a speech target and engagement indicator priority, capturing audio of a speech response to a therapy prompt, and identifying the language of the response using a multilingual language identification model. The method further comprises analyzing the speech response with a language-specific recognition model to extract speech features and classify errors across multiple linguistic and acoustic dimensions. Engagement indicators are extracted and used to compute an engagement score, which, along with the error classifications and speech target, informs a decision model that selects a therapy task. The selected task is presented to the patient, and a subsequent speech response is captured to update error classifications. The decision model is iteratively refined based on therapist input and revised error data, enabling adaptive, personalized therapy progression.

The application discloses a kind of humanoid robot language identification model training method and language identification method, wherein, the training method obtains multi-modal training sample, the multi-modal training sample includes audio mode sample, oral type mode sample and environment mode sample;The multi-modal training sample is encoded to multi-layer cross-modal, obtains several cross-modal encoding features, each cross-modal encoding feature includes acoustic encoding feature, oral type encoding feature and environment encoding feature;Cross-modal fusion is carried out to all the cross-modal encoding features, obtains first fusion feature;According to the environment mode sample and the first fusion feature, the parameter of initialization humanoid robot language identification model is updated, obtains well-trained humanoid robot language identification model.The training method provides a kind of humanoid robot language identification model, which is beneficial to improve the accuracy and robustness of language identification.The application relates to the technical field of intelligent robot.

Embodiments of the present application provide a multilingual self-supervised model pre-training method, system and electronic equipment. The method comprises: inputting unpaired unsupervised speech data selected from a multilingual data set to a language identification network to build a language classifier; extracting target language speech embedding and speech sentence embedding from a target language speech and speech sentence set; determining a training difficulty standard of extended dynamic curriculum learning of the multilingual self-supervised model based on an initial running loss of the multilingual self-supervised model, the target language speech embedding and the speech sentence embedding; and the language classifier performing pre-training of the multilingual self-supervised model in extended dynamic curriculum learning based on the training difficulty standard and a training set of different data amounts dynamically determined from the multilingual data set. The method of the embodiments of the present application makes the multilingual data set more efficient, eliminates potential harmful data on low-resource target language speech, and improves the performance of multilingual self-supervised learning downstream tasks.

The invention provides a real-time dynamic display transcription method and system, electronic equipment and a storage medium. The method comprises the following steps: segmenting a single-channel voice stream into continuous multi-frame voice streams; performing human voice detection on the voice stream to obtain a detection result; if a preset number of continuous target voice streams exist, streaming transcription is carried out on each frame of target voice stream and the acquired human voice stream in real time by using a current language through a streaming transcription model; in the streaming transcription process, accumulated audios are collected every preset time period, and the streaming text in the preset time period is corrected by utilizing an accumulated text obtained by transcription of the accumulated audios until a human voice ending signal is detected; correcting the streaming text in the target time period by using a target accumulated text obtained by transferring the target accumulated audio in the target time period to obtain a target corrected text; decoding the target correction text to obtain and display a final text; and updating the current language by using a language obtained by performing language identification on the target accumulated audio.

The method (400) includes receiving a sequence of acoustic frames (110) as an input to an automatic speech recognition (ASR) model (200). The method also includes generating, by a first encoder (210), a first high-level feature representation (212) for the corresponding acoustic frame. The method also includes generating, by a second encoder (220), a second high-level feature representation (222) for the corresponding first high-level feature representation. The method also includes generating, by a language identification (ID) predictor (230), a language prediction representation (232) based on a combination (231) of the first high-level feature representation and the second high-level feature representation. The method also includes generating, by a first decoder (240a), a first probability distribution (120a) over possible speech recognition hypotheses based on a combination of the second high-level feature representation and the language prediction representation.

This invention discloses a multi-agent content generation method and system driven by cross-cultural dynamic profiling, belonging to the fields of artificial intelligence and cross-cultural information processing technology. The method includes: S1, collecting multilingual text data through social media interfaces and performing structural verification, language identification, text normalization, deduplication, and quality filtering to construct a cross-cultural corpus knowledge base; S2, dividing the corpus into groups based on clustering algorithms, extracting group features, and constructing structured user profiles; S3, scheduling multiple agents to collaboratively perform content generation, trend analysis, and cross-cultural interaction tasks according to user profile tags and rule-based routing; S4, constructing a virtual audience cluster and pre-evaluating the generation strategy using a simulation mechanism that decouples the generation model and the evaluation model; S5, optimizing and iterating the generation strategy based on the evaluation results to form a closed-loop update mechanism. This invention effectively improves the stability and computational accuracy of the cross-language data processing link.

An information handling system includes an embedded controller that detects the presence of a keyboard within the information handling system. The embedded controller also determines a language identification associated with the keyboard. Based on the language identification being different than a current language identification, a processor receives the language identification from the embedded controller. Based on the language identification, the processor configures a localized language for an operating system of the information handling system.

Embodiments of the present application provide an input method and device, electronic equipment and computer storage medium. According to the input scheme provided by the embodiments of the present application, the input text of the user is obtained, the preferred language distribution of the user is determined according to the identity of the user; a pre-trained language identification model is used to determine the first prediction distribution of the language of the input text; according to the first prediction distribution and the preferred language distribution, the second prediction distribution of the language of the input text is generated; and the predicted language of the input text is determined according to the second prediction distribution. By using the preferred language distribution of the user to correct the first prediction distribution of the language identification model, the individualization and accuracy of language identification are enhanced at the user granularity.

A hybrid language identification (HLI) system includes one or more microphones configured to detect an acoustic utterance within an interior of a host system; a speaker operable to broadcast prompts or responses within the interior of the host system; a processor and a memory. A processor performs a method that classifies a sound utterance as a mixed utterance having two or more languages using mixed language detection logic stored in a memory, determines a relative language contribution of the languages, and commands a speaker to broadcast a prompt or response within the interior. The prompts and responses have relative language contributions. The HLI system may be used as part of a vehicle having a vehicle body defining a vehicle interior within which hybrid speech recognition occurs.

The application discloses a patent literature intelligent classification method and system based on semantic understanding, acquires patent literature data and classification system configuration parameters, recognizes high-density semantic areas through text segmentation and information density analysis, and constructs a classification index library; adopts a pre-training model to perform vectorization coding to form a semantic vector space, identifies ambiguous feature points through bidirectional semantic detection to form a theme clustering space; implements label matching analysis on the theme clustering space, and performs priority sorting and disambiguation processing to establish a semantic classification rule library; decomposes a classification matching strategy into core feature and auxiliary feature matching sequences, extracts field attributes of a candidate classification set, and implements weight proportioning to determine classification attribution parameters; extracts semantic mapping rules in combination with source language identification, and realizes cross-language retrieval through semantic alignment, thereby realizing accurate understanding of patent technology semantics and supporting multilingual retrieval.

Embodiments of the present disclosure disclose a code multilingualization method, device, equipment, storage medium and program product, wherein the code multilingualization method comprises: determining a code variable capable of being recognized by a current code running environment; the code variable comprises original language text in source code and translated language text of the original language text in multiple languages; based on target original language text to be processed, the code variable and language identification, determining a to-be-replaced variable corresponding to the target original language text; replacing the target original language text in the source code with the to-be-replaced variable to obtain multilingual source code; the to-be-replaced variable is used to adjust the translated language text corresponding to the target original language text based on the language identification. In this way, the multilingual switching of the original language text in the source code can be intelligently realized, the operation is simple, and the applicability is strong.

This application relates to a method, system, device, and medium for text-level knowledge extraction from long texts, belonging to the field of knowledge extraction technology. The method includes: inputting diverse heterogeneous data, parsing it into text according to a format, and then performing language identification and translation; connecting to a knowledge platform to obtain and structure entity information, and generating large language model prompt words based on prompt word templates; designing example data format conversion sample data, inputting it into the model to generate extraction results, parsing it into RDF triples, and performing entity alignment and disambiguation. This application can accurately extract knowledge from diverse heterogeneous long texts, solve the problem of text fragmentation, and also achieve entity alignment and disambiguation, thereby improving the consistency and quality of knowledge graphs.

Techniques for responding to multilinguistic queries using an agent core are disclosed herein. An agent core is trained and / or fine-tuned in a first language to generate instructions (i.e., commands) for answering a query. A language identification and / or translation model receives queries, identifies languages associated with the queries, and translates the queries to the first language. The agent core generates instructions in the first language based on the translated queries. The instructions include instructions, in the first language, to perform actions such as retrieval, generation, contextual understanding, or calculation, in both the first language and the second language. The results of executing the instructions, including an action performed in the first language and an action performed in one or more second languages, are combined, ranked and / or reranked to generate an answer to the query.

A method, computer program product, and computing system for speech language identification. An input voice signal of a specific language is received. The input speech signal is processed by a plurality of speech recognition processing paths, each speech recognition processing path identifying an associated subset of languages. Using each of the speech recognition processing paths, an input speech signal is processed using machine learning to identify a language that most closely matches a particular language of the input speech signal to produce a plurality of identified languages. An indication of each of the input speech signal and the plurality of identified languages is received in a further speech recognition processing path. The input speech signal is processed using machine learning to identify one of the identified languages as closest matching to the particular language.

Disclosed are a font language identification method, device and storage medium. The font language identification method comprises the following steps: obtaining font attributes of a font to be identified, wherein the font attributes comprise a font name language; identifying a language according to the font name language in the font attributes, and determining the identified language as a language supported by the font to be identified. According to the method provided herein, the same font can be determined to belong to the same language on different operating systems, thereby maintaining the same user experience.

The application discloses a multilingual text classification method, device and equipment and a medium. The method comprises the following steps: obtaining a target text and a pre-trained learning model, wherein the learning model comprises a shared feature extraction network and a plurality of sub-task identification networks; obtaining a sentence vector representation of the target text and a language identification prediction result through the shared feature extraction network; and calling a sub-task identification network corresponding to the language to process the sentence vector representation according to the language identification prediction result, so as to obtain a classification result of the target text, wherein a language self-learning module in the sub-task identification network learns the correlation between a plurality of languages corresponding to the language. The application can integrate the correlation knowledge between a plurality of languages into the model for learning, and classify the multilingual text through the model, so that the multilingual text can be better classified. Accordingly, the application also provides a multilingual text classification device, equipment and medium.