SYSTEM AND METHOD FOR OUT-OF-VOCABULARY PHRASE SUPPORT IN AUTOMATIC SPEECH RECOGNITION

    公开(公告)号:US20210343277A1

    公开(公告)日:2021-11-04

    申请号:US17160278

    申请日:2021-01-27

    Abstract: An electronic device includes an audio sensor, a memory, and at least one processor coupled to the audio sensor and the memory. The at least one processor is configured to receive, via the audio sensor an audio input. The at least one processor is further configured to perform, using an automatic speech recognition (ASR) model and an entity prediction model, out-of-vocabulary prediction of an entity. The at least one processor is further configured to receive an ASR hypothesis including the predicted entity. The at least one processor is further configured to output text including the predicted entity.

    SYSTEM AND METHOD FOR CONTEXT INSERTION FOR CONTRASTIVE SIAMESE NETWORK TRAINING

    公开(公告)号:US20230385546A1

    公开(公告)日:2023-11-30

    申请号:US18315931

    申请日:2023-05-11

    CPC classification number: G06F40/284

    Abstract: A method includes receiving an input utterance that is a continuation of a previous utterance. The method also includes, using a trained Siamese network, determining input utterance embeddings representing tokens from the input utterance, pooling the input utterance embeddings with a context token embedding representing a class associated with the previous utterance to generate a representative input utterance embedding, and determining a representative embedding associated with each of multiple possible classes. Each possible class is associated with first and second threshold boundaries. The method further includes, using the trained Siamese network, determining a similarity score for each possible class based on a distance between the representative input utterance embedding and a selected threshold boundary of the representative embedding for that possible class and identifying a class for the input utterance based on the determined similarity scores. In addition, the method includes performing an action corresponding to the identified class.

    SPLIT KEY AND VALUE SELF-ATTENTION MACHINE LEARNING

    公开(公告)号:US20240370701A1

    公开(公告)日:2024-11-07

    申请号:US18582349

    申请日:2024-02-20

    Abstract: A method includes receiving an input by a self-attention machine learning model and generating a set of queries using the input. This method also includes generating at least one of two sets of keys using the input and two sets of values using the input. This method also includes determining an output of the self-attention machine learning model using the two sets of keys, the two sets of values, or both. Another method includes identifying a query position for the set of queries, identifying a key position for the two sets of keys, and when the query position is determined to be equal to the key position, calculating an attention score using a first set of the two sets of keys, or, when the query position is determined to be unequal to the key position, calculating the attention score using a second set of the two sets of keys.

    SYSTEM AND METHOD FOR DETECTING UNHANDLED APPLICATIONS IN CONTRASTIVE SIAMESE NETWORK TRAINING

    公开(公告)号:US20230386450A1

    公开(公告)日:2023-11-30

    申请号:US18303394

    申请日:2023-04-19

    CPC classification number: G10L15/063 G10L2015/0636 G10L15/183

    Abstract: A method includes determining, using at least one processing device of an electronic device, a target embedding vector for each class of a plurality of classes. The method also includes generating, using the at least one processing device, an utterance embedding vector using a pre-trained language model, where the utterance embedding vector represents an input utterance associated with an expected class. The method further includes obtaining, using the at least one processing device, a predicted class associated with the input utterance based on distances of the utterance embedding vector to spatial parameters representing the plurality of classes, where the spatial parameter of each class is based on the target embedding vector associated with that class. In addition, the method includes updating, using the at least one processing device, parameters of the language model based on a difference between the predicted class and the expected class.

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