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Online incremental adaptation of deep neural networks using auxiliary Gaussian mixture models in speech recognition

机译:使用辅助高斯混合模型进行语音识别的深层神经网络在线增量自适应

摘要

Methods and systems for online incremental adaptation of neural networks using Gaussian mixture models in speech recognition are described. In an example, a computing device may be configured to receive an audio signal and a subsequent audio signal, both signals having speech content. The computing device may be configured to apply a speaker-specific feature transform to the audio signal to obtain a transformed audio signal. The speaker-specific feature transform may be configured to include speaker-specific speech characteristics of a speaker-profile relating to the speech content. Further, the computing device may be configured to process the transformed audio signal using a neural network trained to estimate a respective speech content of the audio signal. Based on outputs of the neural network, the computing device may be configured to modify the speaker-specific feature transform, and apply the modified speaker-specific feature transform to a subsequent audio signal.
机译:描述了在语音识别中使用高斯混合模型进行神经网络在线增量自适应的方法和系统。在示例中,计算设备可以被配置为接收音频信号和随后的音频信号,这两个信号均具有语音内容。计算设备可以被配置为将扬声器特定的特征变换应用于音频信号以获得变换后的音频信号。特定于说话者的特征变换可以被配置为包括与语音内容有关的说话者简档的特定于说话者的语音特征。此外,计算设备可以被配置为使用经训练以估计音频信号的相应语音内容的神经网络来处理经变换的音频信号。基于神经网络的输出,计算设备可以被配置为修改说话者特定特征变换,并且将修改后的说话者特定特征变换应用于随后的音频信号。

著录项

  • 公开/公告号US9466292B1

    专利类型

  • 公开/公告日2016-10-11

    原文格式PDF

  • 申请/专利权人 GOOGLE INC.;

    申请/专利号US201313886620

  • 发明设计人 PETAR ALEKSIC;XIN LEI;

    申请日2013-05-03

  • 分类号G10L15/00;G10L15/16;

  • 国家 US

  • 入库时间 2022-08-21 14:35:21

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