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    Acoustic model training
    2.
    发明授权
    Acoustic model training 有权
    声学模型训练

    公开(公告)号：US09495955B1

    公开(公告)日：2016-11-15

    申请号：US13733084

    申请日：2013-01-02

    Applicant: Amazon Technologies, Inc.

    Inventor： Frederick Victor Weber ,  Jeffrey Penrod Adams

    IPC: G10L15/00 ,  G10L15/06

    CPC classification number: G10L15/063

    Abstract: Features are disclosed for generating acoustic models from an existing corpus of data. Methods for generating the acoustic models can include receiving at least one characteristic of a desired acoustic model, selecting training utterances corresponding to the characteristic from a corpus comprising audio data and corresponding transcription data, and generating an acoustic model based on the selected training utterances.

    Abstract translation: 公开了用于从现有数据语料库产生声学模型的特征。 用于生成声学模型的方法可以包括：接收期望的声学模型的至少一个特征，从包括音频数据和对应的转录数据的语料库中选择与特征对应的训练语音，以及基于所选择的训练语音来生成声学模型。

    AUTOMATIC SPEAKER IDENTIFICATION USING SPEECH RECOGNITION FEATURES
    4.
    发明申请
    AUTOMATIC SPEAKER IDENTIFICATION USING SPEECH RECOGNITION FEATURES 审中-公开

    公开(公告)号：US20200349957A1

    公开(公告)日：2020-11-05

    申请号：US15929795

    申请日：2020-05-21

    Applicant: Amazon Technologies, Inc.

    Inventor： Hugh Evan Secker-Walker ,  Baiyang Liu ,  Frederick Victor Weber

    IPC: G10L17/06 ,  G10L17/12 ,  G10L17/02 ,  G10L17/16 ,  G10L15/18 ,  G10L17/22

    Abstract: Features are disclosed for automatically identifying a speaker. Artifacts of automatic speech recognition (“ASR”) and/or other automatically determined information may be processed against individual user profiles or models. Scores may be determined reflecting the likelihood that individual users made an utterance. The scores can be based on, e.g., individual components of Gaussian mixture models (“GMMs”) that score best for frames of audio data of an utterance. A user associated with the highest likelihood score for a particular utterance can be identified as the speaker of the utterance. Information regarding the identified user can be provided to components of a spoken language processing system, separate applications, etc.

    AUTOMATIC SPEAKER IDENTIFICATION USING SPEECH RECOGNITION FEATURES
    7.
    发明申请
    AUTOMATIC SPEAKER IDENTIFICATION USING SPEECH RECOGNITION FEATURES 审中-公开

    公开(公告)号：US20190378517A1

    公开(公告)日：2019-12-12

    申请号：US16448788

    申请日：2019-06-21

    Applicant: Amazon Technologies, Inc.

    Inventor： Hugh Evan Secker-Walker ,  Baiyang Liu ,  Frederick Victor Weber

    IPC: G10L17/06 ,  G10L17/22 ,  G10L15/18 ,  G10L17/16 ,  G10L17/02 ,  G10L17/12

    Abstract: Features are disclosed for automatically identifying a speaker. Artifacts of automatic speech recognition (“ASR”) and/or other automatically determined information may be processed against individual user profiles or models. Scores may be determined reflecting the likelihood that individual users made an utterance. The scores can be based on, e.g., individual components of Gaussian mixture models (“GMMs”) that score best for frames of audio data of an utterance. A user associated with the highest likelihood score for a particular utterance can be identified as the speaker of the utterance. Information regarding the identified user can be provided to components of a spoken language processing system, separate applications, etc.

    Automatic speaker identification using speech recognition features
    9.
    发明授权
    Automatic speaker identification using speech recognition features 有权
    自动扬声器识别使用语音识别功能

    公开(公告)号：US09558749B1

    公开(公告)日：2017-01-31

    申请号：US13957257

    申请日：2013-08-01

    Applicant: Amazon Technologies, Inc.

    Inventor： Hugh Evan Secker-Walker ,  Baiyang Liu ,  Frederick Victor Weber

    IPC: G10L15/00 ,  G10L17/00 ,  G10L17/12

    CPC classification number: G10L17/06 ,  G10L15/18 ,  G10L15/20 ,  G10L15/26 ,  G10L17/02 ,  G10L17/12 ,  G10L17/16 ,  G10L17/22 ,  G10L2015/025 ,  G10L2015/088

    Abstract: Features are disclosed for automatically identifying a speaker. Artifacts of automatic speech recognition (“ASR”) and/or other automatically determined information may be processed against individual user profiles or models. Scores may be determined reflecting the likelihood that individual users made an utterance. The scores can be based on, e.g., individual components of Gaussian mixture models (“GMMs”) that score best for frames of audio data of an utterance. A user associated with the highest likelihood score for a particular utterance can be identified as the speaker of the utterance. Information regarding the identified user can be provided to components of a spoken language processing system, separate applications, etc.

    Abstract translation: 公开了用于自动识别扬声器的特征。 自动语音识别（“ASR”）和/或其他自动确定的信息的工件可以针对各个用户简档或模型进行处理。 可以确定反映个人用户发声的可能性的得分。 分数可以基于例如对于语音的音频数据的帧最佳得分的高斯混合模型（“GMM”）的各个组件。 与特定话语的最高似然分数相关联的用户可以被识别为话语的说话者。 关于识别的用户的信息可以被提供给口语处理系统的组件，单独的应用等。

    Adaptive neural network speech recognition models
    10.
    发明授权
    Adaptive neural network speech recognition models 有权
    自适应神经网络语音识别模型

    公开(公告)号：US09153231B1

    公开(公告)日：2015-10-06

    申请号：US13836141

    申请日：2013-03-15

    Applicant: Amazon Technologies, Inc.

    Inventor： Stan Weidner Salvador ,  Frederick Victor Weber

    IPC: G10L15/00 ,  G10L15/16 ,  G10L15/065 ,  G06N3/02

    CPC classification number: G10L15/16 ,  G06N3/02 ,  G10L15/065 ,  G10L15/187 ,  G10L15/197

    Abstract: Neural networks may be used in certain automatic speech recognition systems. To improve performance of these neural networks, they may be updated/retrained during run time by training the neural network based on the output of a speech recognition system or based on the output of the neural networks themselves. The outputs may include weighted outputs, lattices, weighted N-best lists, or the like. The neural networks may be acoustic model neural networks or language model neural networks. The neural networks may be retrained after each pass through the network, after each utterance, or in varying time scales.

    Abstract translation: 神经网络可用于某些自动语音识别系统。 为了提高这些神经网络的性能，可以通过基于语音识别系统的输出或基于神经网络本身的输出来训练神经网络来在运行时更新/再训练。 输出可以包括加权输出，格子，加权N最佳列表等。 神经网络可以是声学模型神经网络或语言模型神经网络。 神经网络可以在每次通过网络之后，在每个话语之后或在不同的时间尺度上被再培训。

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