669 results about "Multiple language" patented technology

A novel method provides an AI language virtual agent having self-improvement features and which uses language modeling and tree search techniques. The AI language virtual agent exchanges textual discussion with users and other simulated agents. The method includes receiving a current situational description depicting natural language user input, temperament qualities and textual tendencies of the virtual agent, and indicia regarding subject matter context of a present conversation. The indicia regarding subject matter context include textual logs from recent conversational exchanges. The current situational description includes audio, visual, and tactile inputs collected proximate to the virtual agent. The method preferably utilizes an MCTS tree search in combination with self-moving modules, one or more language models, tree search techniques outputting textual responses to the current situation description, and the virtual agent responding with textual expression to verbal input in combination with the audio, visual, tactile, and other sensory inputs.

Approaches presented herein provide for the performance of quality assurance-related tasks with respect to a representation of an environment, such as a set of generated map data. In particular, a language model can be used to generate a tokenized representation of a map using information such as the semantics, topology, and / or geometry determinable from the map data. A language model-generated representation can comply with real-world rules and constructs, and can account for omissions or errors in the input data based upon known relationships and semantics for various objects in the environment. One or more language models can be used to not only identify potential issues in the map data, but also to make recommendations for modifications and / or to generate plaintext descriptions of the issues, modifications, or recommendations to assist a human reviewer in addressing the issues.

The invention discloses a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method, and relates to the field of cross-border contract compliance auditing. In the multi-modal data access step, customs codes, laws and regulations and other multi-source data are collected, and 18 kinds of language contract texts are analyzed; in the cross-language semantic alignment step, a knowledge graph is constructed, and multi-language legal concept mapping is achieved; in the compliance risk reasoning step, a rule engine and an agent cooperatively check a contract, and the compliance conclusion confidence is calculated; the dynamic risk assessment step adopts an LSTM network to analyze historical data and predict a risk trend; in the multi-language report generation step, a multi-format bilingual or multilingual report is generated based on a template engine, encrypted and archived. According to the invention, cross-border contracts are audited efficiently and intelligently, dynamic adaptation laws and regulations are analyzed in multiple languages, compliance risks are identified accurately, and a multi-language report is generated quickly; therefore, the checking efficiency is improved, the manual workload is reduced, the compliance risk is reduced, and the enterprise cross-border business competitiveness and the risk response capability are enhanced.

The invention discloses an automatic dubbing method and system based on generative AI. The method comprises the following steps: separating a human voice audio, a background voice audio and a silent video part from an original video, automatically identifying and translating text content in the audio, and combining a translated text with a corresponding time period to generate a translated subtitle text; segmenting the voice audio into segments, marking character attributes for each segmented segment according to voiceprint features, and evaluating the voice quality at the same time; converting the segmented subtitle text into timbre audios of a target language by using a TTS technology, and connecting and merging the audios according to the starting and ending time of the subtitles; and finally, synthesizing the generated target audio with the background sound audio and the silent video to obtain a complete target video. The above steps efficiently complete dubbing work of multiple languages, multiple roles and multiple scenes, and improve dubbing quality and dubbing efficiency.

Aspects of the present disclosure relate to detecting hallucinations in language model outputs. Embodiments include receiving a user query. Embodiments further include prompting a language processing machine learning model to generate responses to the user query in each language of a set of multiple languages. Embodiments further include receiving the responses from the language processing machine learning model in response to the prompting. Embodiments further include creating embedding representations of the responses. Embodiments further include calculating, based on the embedding representations, a degree of semantic similarity between the responses. Embodiments further include determining that a response of the responses contains a model hallucination based on comparing the degree of semantic similarity between the responses to a threshold.

In various examples, synthesizing speech in multiple languages in conversational AI systems and applications is described herein. Systems and methods are disclosed that use one or more models to synthesize speech from a first language spoken by a speaker to a second, target language selected by the speaker. In some examples, to perform the translation, the model(s) may disentangle one or more attributes associated with speech from speakers, such as speakers' identities, speakers' accents, and text associated with the speech. Additionally, the model(s) may allow for fine-grained control of additional attributes associated with output speech, such as one or more frequencies, one or more energies, and one or more phoneme durations. Furthermore, the model(s) may be configured to use the accent associated with the target language when generating text, such as when aligning text encodings with one or more phonemes.

Described herein are systems and methods for identifying the severity of a mental health condition or symptoms of same by listening to a human-to-human conversation by receiving conversation data, processing the conversation data to generate a language model output and / or an acoustic model output using one or more language models and / or acoustic models. Further described herein are systems and methods for automatically tracking and providing analytics on self-report questionnaires administered during the conversation.

The invention belongs to the technical field of document content extraction, and particularly discloses a document content extraction method and system based on multimodal model collaboration, a terminal and a medium. Comprising the following steps: identifying the type of an input to-be-processed document, and judging the document type; on the basis of the type identification result, calling a multi-modal model to analyze the document content, and outputting space coordinates, visual features and semantic features of document elements; generating a content sequence according with a reading habit through a semantic sequence reconstruction algorithm; paragraph boundary detection, paragraph recombination and semantic association modeling of charts and texts are completed based on the multilayer attention network and the graph neural network; grammar error correction, format optimization and title hierarchy generation are carried out by using a large language model and a hierarchical classification network; and converting the identification result into a structured output file. According to the method, the processing requirements of different types of documents can be considered, and high-precision analysis and efficient output are realized under the scenes of complex layouts, multiple languages and formula tables.

The invention discloses a multi-language knowledge graph construction method, system and device and a medium. The method comprises the steps of obtaining text data, voice data and image data; performing standardization processing on the text data, the voice data and the image data to obtain multi-source heterogeneous data after format unification; mapping the multi-source heterogeneous data with the unified format into a unified semantic representation space, and performing semantic annotation to generate a corresponding semantic annotation result; the embedding representations of different languages are aligned based on a comparative learning technology, the embedding of multiple languages is mapped to a unified semantic space, and a semantic annotation result is updated; and constructing a knowledge graph based on the updated semantic annotation result, and updating the knowledge graph in real time based on an incremental updating algorithm. According to the technical scheme, precise analysis of complex semantics and efficient annotation of unseen knowledge can be achieved, dynamic real-time updating of the knowledge graph is achieved, and the response speed and knowledge updating efficiency of a system are remarkably improved.

Approaches presented herein provide for the automated, end-to-end generation of map data based at least in part on sensor data captured for an environment. At least one language model can be used to generate a text-based, tokenized description of the environment that includes semantics, topology, geometry, and / or other information for the environment. A generation pipeline can use one or more language models in one or more stages, and the data passed between stages can be in a determined tokenized representation format, as may correspond to a tokenized text string in a specific structured language.

Systems and methods are provided for implementing URL embeddings for aligning parallel documents that are corresponding web pages in at least two different languages. A computing system uses a pre-trained model of an AI system to calculate URL embeddings for each URL among a plurality of URLs. The system identifies, based on closeness of the points represented by the URL embeddings, a set of candidate parallel URLs by analyzing the URL embeddings for the plurality of URLs or for a second plurality of URLs that has been partitioned into a cluster, using a clustering algorithm. A set of parallel URLs, associated with the parallel documents, is selected from the identified set of candidate parallel URLs. Document text and / or parallel sentences are extracted from web documents associated with the set of parallel URLs to train a machine translation model for translating between two or more languages.

A method of leveraging improved conversational bots in a contact center system according to an embodiment includes performing knowledge distillation through machine learning to teach a student artificial intelligence model based on a teacher artificial intelligence model and reduce a size of an initial multilingual vocabulary, wherein the student artificial intelligence model includes fewer machine learning embedding layers than the teacher artificial intelligence model, removing tokens from the initial multilingual vocabulary based on a grouping of languages with linguistic similarities to reduce the size of the initial multilingual vocabulary, parsing user text from a human user into one or more tokens, identifying token indexes associated with the respective one or more tokens in a reduced multilingual vocabulary, determining embedding values associated with the identified token indexes, and generating a multilingual embedding output for the user text indicative of user intent based on the embedding values using machine learning.

Systems and methods for generating speaker video and audio in multiple languages for videoconferencing are provided. For example, a computing device can access a speaker speech audio signal that includes a speaker speech in a first language, a video of the speaker and a translated speech audio signal of the speaker speech in a second language. The computing device generates, based on the translated speech audio signal, a converted translated speech audio signal that includes a speech in the second language having voice characteristics in the speaker speech. The computing device further generates a lip-synched speaker video based on the video of the speaker and the converted translated speech audio signal. Lip movements in the lip-synched speaker video correspond to the converted translated speech audio signal. The converted translated speech audio signal and the lip-synched speaker video are transmitted to a video conference provider configured to host the video conference.

The invention provides a digital human lip shape synchronization method based on voice driving. The method comprises the following steps: acquiring a voice signal; voice features related to lip shape changes in the voice signals are extracted; mapping the extracted voice features into lip shape change data; and applying the lip shape change data to the digital human model and carrying out real-time rendering to finish lip shape synchronization. According to the method, the voice signal characteristics are directly analyzed, subtle changes of pronunciation can be more accurately captured, and more natural and smooth lip-shaped actions are generated; the method is suitable for a multi-language environment, and especially improves the processing effect of tone languages such as Chinese and the like. An optimized signal processing and mapping algorithm improves the lip shape synchronization speed, and the real-time interaction requirement is met; a complicated phoneme identification process is not needed, and the technical implementation difficulty and the maintenance cost are reduced; through more accurate and natural lip shape synchronization, the overall performance and interaction naturalness of the digital human are remarkably improved.

Disclosed are apparatuses, systems, and techniques that leverage one or more language models (LMs)—such as large language models (LLMs—for efficient multi-speaker speech recognition. The techniques include processing, using a speaker diarization model, an audio feature to generate a first association of the audio feature with one or more prospective speakers, the audio feature being representative of one or more spoken words. The techniques further include providing, to an LM, a first prompt requesting the LM to identify a second association of the one or more spoken words with the one or more prospective speakers and receiving, from the LM, a first response identifying the second association of the one or more spoken words with the one or more prospective speakers. The techniques further include determining, using the first association and the second association, one or more speakers that produced the one or more spoken words.

Methods, systems, and computer storage media for providing speech synthesis using a code-mixed speech engine in a speech synthesis system. A code-mixed speech engine supports generating natural and intelligible speech in a target speaker voice—for code-mixed-text of two or more languages—based on a code-mixed speech model that supports both code-mixing and cross-locale voice transfer scenarios. In operation, code-mixed training data associated with a plurality of different languages is accessed. A code-mixed speech model—associated with a training engine and an inference engine that support generating code-mixed synthesized speech—is generated. The code-mixed speech model is deployed. A request being received for synthesized speech of a speech synthesis service. An instance of code-mixed synthesized speech is generated. The instance of code-mixed synthesized speech is generated using the code-mixed speech model. The instance of code-mixed synthesized speech is communicated for output on an interface associated with the speech synthesis service.

To provide a system that extracts useful information from text data and efficiently performs translation and information retrieval between multiple languages.SOLUTION: A system according to the embodiment comprises an analysis unit, an extraction unit, a translation unit, an information search unit, and a provision unit. The analysis unit extracts information from text data, and performs topic modeling and emotion analysis. The extraction unit summarizes the information extracted by the analysis unit, and extracts main points. The translation unit translates the text data extracted by the extraction unit between multiple languages. The information search unit searches for and acquires the text data translated by the translation unit in response to input from a user. The provision unit provides the information acquired by the information search unit.SELECTED DRAWING: Figure 1

One example method includes receiving an audio stream comprising speech; generating, by automatic speech recognition (“ASR”) software, a plurality of hypotheses, each hypothesis comprising a transcription of a first portion of the speech; rescoring, using a first trained language model, each hypothesis of the plurality of hypotheses; and responsive to a first hypothesis not satisfying a threshold, generating and outputting, using a trained large language model (“LLM”), a final transcription based on the plurality of hypotheses.

The invention discloses a method of connecting multilingual translation module / s with large language model / s for optimized output generation and output translation of user inputs in the user preferred language. This method is carried out by a worker module which is connected to large language model / s and third-party translation module / s to provide seamless translation and text generation capabilities for a user input. Existing machine translation systems and text generation models have limitations in handling multiple languages and integrating translation with complex text generation. The proposed invention addresses these limitations by connecting and integrating user input translation, text generation, and subsequent translation of the output to the user's original language. The invention offers a more coherent and contextually relevant interaction for users who prefer to communicate in their native language with generative text AI by bridging the gap between translation and text generation.

Embodiments are provided for building a configurable multilingual model. A computing system obtains a plurality of language-specific automatic speech recognition modules and a universal automatic speech recognition module trained on a multi-language training dataset comprising training data corresponding to each of the plurality of different languages. The computing system then compiles the universal automatic speech recognition module with the plurality of language-specific automatic speech recognition modules to generate a configurable multilingual model that is configured to selectively and dynamically utilize a sub-set of the plurality of language-specific automatic speech recognition modules with the universal automatic speech recognition module to process audio content in response to user input identifying one or more target languages associated with the audio content.

The embodiment of the invention discloses a voice recognition method and device, electronic equipment and a storage medium. The method comprises the steps that to-be-recognized voice is acquired; recognizing the voice to be recognized through the mixed voice recognition model to obtain a voice recognition result; the hybrid speech recognition model comprises a target coding module, a hybrid expert model MoE module and a target decoding module, the MoE module comprises a routing network and a plurality of expert network groups in one-to-one correspondence with multiple language types, and the routing network is used for carrying out target coding according to initial speech characterization; and determining a target expert network group corresponding to the initial voice representation from the plurality of expert network groups, and processing the initial voice representation through the target expert network group to obtain a target voice representation. According to the embodiment of the invention, the method improves the recognition accuracy of the mixed voice containing a plurality of languages.

Disclosed is a method of artificial intelligence, Al, assisted natural-language text processing. The method comprises entering (402) user-generated text input into an electronic document during a user-writing activity while the electronic document is displayed on a graphical user interface, the user-generated text input comprising natural-language text and at least one placeholder. The method comprises determining (404) that the user-writing activity is completed. The method comprises starting (406) a machine-writing activity comprising entering (408) machine-generated text input from an Al system (104) comprising one or more language models (106) into the electronic document replacing the at least one placeholder (one after the other) in the electronic document with natural-language text.

The invention provides a double-screen different-display real-time translation machine implementation method and device, and the method comprises the following steps: 1, obtaining and starting a double-screen different-display real-time translation machine, and initializing an SOC SSD2386 chip, two touch screens, a plurality of microphones distributed near the two touch screens and a storage unit of the double-screen different-display real-time translation machine; by adopting a double-screen different display technology, the target language text information can be respectively and clearly displayed on the touch screen of the opposite user, so that the situation of image disorder caused by mixed display of multiple languages on the same screen is avoided, the information reading speed and accuracy of the user are greatly improved, and the communication fluency and efficiency are ensured; according to the method, the target language text information is individually set according to the display preference of the user, the font size, the color and the like are adjusted, and only the translation result required by the opposite user is displayed, so that the interference of irrelevant information on the user is effectively avoided, the user experience is improved, and the smooth communication is promoted.

According to the feature fusion speech synthesis method and device based on the VITS, the expressive force and naturalness of speech can be improved, high-quality speech which is more natural, smoother and rich in expressive force is generated, and the expression is particularly outstanding in speech synthesis of multiple languages, multiple emotion expressions and complex contexts. The method comprises the following steps: (1) generating rhythm features matched with input text information according to the input text information; (2) establishing a deep learning model based on Transform; (3) comprehensively processing the rhythm features and the text features by adopting a multi-modal feature fusion method; (4) inputting the comprehensive feature vector into a speech synthesis network based on a variational auto-encoder for processing, wherein the speech synthesis network is trained by adopting a generative adversarial network and a training method based on maximum likelihood; and (5) adopting a neural network model of a random time length predictor, and performing input data transformation through a multi-layer normalized flow structure.

The invention relates to a method for improving the internal accuracy of a response from an artificial intelligence (AI) trained in multiple languages, in particular a generative AI trained in multiple languages. The invention further relates to a data processing system for carrying out the method.

The invention provides an intelligent loudspeaker box permission design management method and system. The method comprises the following steps: collecting user audio data and preprocessing the audio data; a user-situation joint feature vector is constructed to carry out recognition precision adjustment; if a complex voice environment with multiple languages and multiple accents appears, designing a fuzzy matching and dynamic adjustment model to dynamically manage the authority of the intelligent sound box in real time; a behavior monitoring and anomaly detection model is constructed, user permissions are analyzed in real time through feature engineering and time sequence modeling, and permission levels are dynamically adjusted according to anomaly detection and behavior monitoring results; proposing a mapping function of an emotional state and permission response to dynamically adjust the permission level, and immediately applying new permission configuration by the intelligent sound box; and constructing a self-adaptive permission optimization model based on reinforcement learning, and performing self-adaptive learning on the permission strategy of the intelligent sound box through reinforcement learning to perform long-term strategy optimization. The wide application requirements of the intelligent loudspeaker box in diversified scenes such as families and offices are met.

A document information extraction service can utilize an LLM to provide support for extracting information from documents in multiple languages. A trained first machine learning model can map data extracted from a first master document of a first document type in a first language. The mappings can be corrected via user input to obtain ground truth data for the master document. The ground truth data can be translated into a second language and optionally corrected to obtain translated ground truth data. An LLM can generate a training dataset of fake documents of the first document type that contain text in the second language based at least in part on the translated ground truth data. The trained first machine learning model can be trained further with the training dataset and deployed to extract data from documents of the first document type that contain text in the second language.

To provide a multilingual voice distribution method capable of translating a voice uttered by a speaker into other languages, minimizing the occurrence of a time lag accompanying the translation, and transmitting the voice to a plurality of recipients without delay. [Solution] Speech uttered by a speaker is input to a speech terminal 1 and transmitted with a PTT (Press to Talk) button held down. The speaker's speech is input to a translation engine 3 and converted to text by a text conversion unit 3a. The text converted from the speech is translated into multiple languages ​​by a multilingual translation unit 3b and converted into speech in each language by a speech synthesis engine unit 3c for each language. The converted multilingual speech is input to a speech distribution server 4, and the multilingual speech is distributed in a preselected language by a speech distribution unit 4a, and the translated and distributed speech is received by a listening terminal 5 and transmitted to the listener.

The invention relates to the technical field of artificial intelligence, provides a multi-language task execution method, device and equipment and a medium, is applied to financial and medical health care service scenes, and can acquire text data and visual action data of multiple languages according to an execution instruction and perform preprocessing to realize standardized processing of multi-modal data; constructing a cross-language word vector semantic space based on adversarial training according to the multi-language features to realize preliminary word vector alignment; performing multi-language semantic alignment on the multi-language features according to a cross-language word vector semantic space, further breaking language barriers, and realizing depth mapping and alignment among different language semantics; a multi-language culture knowledge graph is constructed, so that culture knowledge is introduced, and the culture perception ability is improved; fusion is carried out through a gating fusion mechanism, fusion features with language attributes and cultural attributes can be obtained, and therefore multi-language tasks can be executed more accurately by integrating multi-language information and cultural knowledge.

To provide a system capable of highly accurate translation in real time and coping with a plurality of languages.SOLUTION: A data processing system includes a data processing device 12 and a smart device 14. In specific processing by a specific processing section 290 of the data processing device 12, when a user plays a moving image, a microphone acquires voice data of the moving image in real time, the acquired voice data is transmitted to a voice recognition module, which converts the data into text data, the converted text data is transmitted to a translation module, the translation module acquires data on a necessary language from a language database to translate the data into a designated language, the translated text data is output to a display to present the text data to the user.SELECTED DRAWING: Figure 2