14 results about "More language" patented technology

System and methods for generating a cross-domain multilingual model are disclosed. In some embodiments, a disclosed method includes: storing, in a database, a plurality of first utterances associated with a first language, training a first model using the plurality of first utterances, the first model being associated with the first language, generating, using the first model, a plurality of first representations associated with the plurality of first utterances, training a second model, using the plurality of first representations, the second model being associated with a plurality of second languages, receiving, using the second model, a second utterance in the second language, and generating, using the second model, a response in one or more languages of the plurality of second languages.

Even for language models for which evaluation information is unavailable, performance estimation can be performed appropriately. [Solution] The information processing device comprises: a first acquisition means for acquiring evaluation information of at least one of one or more language models relating to at least one of one or more problems; a second acquisition means for acquiring metadata relating to at least one of the one or more language models; and an estimation means for performing performance estimation processing relating to at least one of the one or more language models by referring to the evaluation information and the metadata.

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.

This specification describes a system and method for identifying a mental health state or the severity of its symptoms by listening to person-to-person conversations by receiving conversational data and processing the conversational data using one or more language models and / or acoustic models to generate language model outputs and / or acoustic model outputs. Furthermore, this specification describes a system and method for automatically tracking and providing analysis of self-report questionnaires administered during conversations.

Techniques for language learning are disclosed. An apparatus 104 is configured to present an image associated with a learning objective, generate one or more language prompts based on the image, receive a user response to the one or more language prompts in a target language, evaluate the user response to determine one or more language proficiency indicators, generate individualized feedback based on the evaluation of the user response, and select a subsequent prompt or task based on the feedback and the learning objective.

Methods and apparatuses for federated language models are provided. A query including a question and a proposed answer is accessed for evaluation using machine learning. A corpus is accessed, and a set of hyperedges is generated based on the corpus of information, where each hyperedge links a set of related concepts from the corpus. Based on the question, a context for the question is identified, where the context comprises a set of relevant hyperedges. A set of candidate answers is generated based on processing the question and the context using one or more language models. A set of regression scores is generated based on processing the question, the context, and the proposed answer using one or more regression models. The proposed answer is evaluated based on the candidate answers and the regression scores. Based on the evaluation, an indication that the proposed answer is not valid is output.

A method, computer system, and a computer program product are provided for responding to a language input query with ad hoc enriched term data. The technique comprises extracting information relating to the language input query using a Language Support Assistance Service. The extracted information includes one or more language terms requiring further support and an associated request type. This is identified from metadata relating to the language input query. Information is provided to a Term Related Corpus Data Service that includes one or more language terms requiring further support and the identified request type and any identified sources. The Term Related Corpus Data Service returns one or more ad hoc enriched terms that are tailored to the one or more language terms requiring further support and is according to the associated request type. The Language Support Assistance Service provides a response to the language input query.

Methods and apparatuses for federated language models are provided. A query including a question and a proposed answer is accessed for evaluation using machine learning. A corpus is accessed, and a set of hyperedges is generated based on the corpus of information, where each hyperedge links a set of related concepts from the corpus. Based on the question, a context for the question is identified, where the context comprises a set of relevant hyperedges. A set of candidate answers is generated based on processing the question and the context using one or more language models. A set of regression scores is generated based on processing the question, the context, and the proposed answer using one or more regression models. The proposed answer is evaluated based on the candidate answers and the regression scores. Based on the evaluation, an indication that the proposed answer is not valid is output.

Systems and methods for automated assessment of one or more language models is described herein. A method can comprise: generating, using a first language model and based at least in part on an input prompt, a plurality of criterion candidates for evaluating an output from the language model(s); ranking, using a second language model, the plurality of criterion candidates a first time, thereby producing a first set of ranks; after producing the first set of ranks, ranking, using the second language model, the plurality of criterion candidates a second time after the first time, thereby producing a second set of ranks; determining, based on the first set of ranks and the second set of ranks, at least one assessment metric to evaluate the output from the language model(s); and evaluating, based at least in part on the at least one assessment metric, a performance of the language model(s).

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.

Disclosed are systems and methods including computing-processes executing machine-learning architectures for voice biometrics, in which the machine-learning architecture implements one or more language compensation functions. Embodiments include an embedding extraction engine (sometimes referred to as an “embedding extractor”) that extracts speaker embeddings and determines a speaker similarity score for determine or verifying the likelihood that speakers in different audio signals are the same speaker. The machine-learning architecture further includes a multi-class language classifier that determines a language likelihood score that indicates the likelihood that a particular audio signal includes a spoken language. The features and functions of the machine-learning architecture described herein may implement the various language compensation techniques to provide more accurate speaker recognition results, regardless of the language spoken by the speaker.

A method, a structure, and a computer system for multilingual learning. The exemplary embodiments may include training, for each language in a set of two or more languages, a model for a task and identifying one or more important words appearing in at least two of the models. The exemplary embodiments may further include weighting one or more conflicts and one or more overlaps between the one or more important words, as well as generating a cluster of at least two languages of the set based on an aggregate of the weighting.

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 for broadcasting a prompt or response within the interior of the host system, a processor, and memory. The processor executes a method that uses hybrid language detection logic stored in the memory to classify the acoustic utterance as a hybrid utterance having two or more languages, determine a relative language contribution of the languages, and command the speaker to broadcast the prompt or response within the interior. The prompt or response has the relative language contribution. The HLI system may be used as part of a vehicle having a vehicle body defining a vehicle interior, with the hybrid speech recognition occurring within the vehicle interior.

Techniques for language learning are disclosed. An apparatus is configured to present an image associated with a learning objective, generate one or more language prompts based on the image, receive a user response to the one or more language prompts in a target language, evaluate the user response to determine one or more language proficiency indicators, generate individualized feedback based on the evaluation of the user response, and select a subsequent prompt or task based on the feedback and the learning objective.