MULTI-LAYERED MACHINE LEARNING SYSTEM TO SUPPORT ENSEMBLE LEARNING

    公开(公告)号:US20200042903A1

    公开(公告)日:2020-02-06

    申请号:US16217362

    申请日:2018-12-12

    Abstract: A method includes providing input data to a plurality of base models to generate a plurality of intermediate outputs. The base models are non-linear in that different base models are specialized differently such that the different base models are complementary to one another. Each of the base models is generated using a different base classification algorithm in a multi-layered machine learning system. The method also includes processing the intermediate outputs using a fusion model to generate a final output associated with the input data. The fusion model is generated using a meta classification algorithm in the multi-layered machine learning system. The method may also include training the classification algorithms, where training data used by each of at least one of the base classification algorithms is selected based on an uncertainty associated with at least one other of the base classification algorithms.

    Multi-layered machine learning system to support ensemble learning

    公开(公告)号:US11741398B2

    公开(公告)日:2023-08-29

    申请号:US16217362

    申请日:2018-12-12

    CPC classification number: G06N20/20

    Abstract: A method includes providing input data to a plurality of base models to generate a plurality of intermediate outputs. The base models are non-linear in that different base models are specialized differently such that the different base models are complementary to one another. Each of the base models is generated using a different base classification algorithm in a multi-layered machine learning system. The method also includes processing the intermediate outputs using a fusion model to generate a final output associated with the input data. The fusion model is generated using a meta classification algorithm in the multi-layered machine learning system. The method may also include training the classification algorithms, where training data used by each of at least one of the base classification algorithms is selected based on an uncertainty associated with at least one other of the base classification algorithms.

    System and method for multi-spoken language detection

    公开(公告)号:US11322136B2

    公开(公告)日:2022-05-03

    申请号:US16731488

    申请日:2019-12-31

    Abstract: A method includes performing, using at least one processor, feature extraction of input audio data to identify extracted features associated with the input audio data. The method also includes detecting, using the at least one processor, a language associated with each of multiple portions of the input audio data by processing the extracted features using a plurality of language models, where each language model is associated with a different language. In addition, the method includes directing, using the at least one processor, each portion of the input audio data to one of a plurality of automatic speech recognition (ASR) models based on the language associated with the portion of the input audio data.

    SYSTEM AND METHOD FOR MULTI-SPOKEN LANGUAGE DETECTION

    公开(公告)号:US20200219492A1

    公开(公告)日:2020-07-09

    申请号:US16731488

    申请日:2019-12-31

    Abstract: A method includes performing, using at least one processor, feature extraction of input audio data to identify extracted features associated with the input audio data. The method also includes detecting, using the at least one processor, a language associated with each of multiple portions of the input audio data by processing the extracted features using a plurality of language models, where each language model is associated with a different language. In addition, the method includes directing, using the at least one processor, each portion of the input audio data to one of a plurality of automatic speech recognition (ASR) models based on the language associated with the portion of the input audio data.

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