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Complex evolution recurrent neural networks

机译:复杂的演变经常性神经网络

摘要

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using complex evolution recurrent neural networks. In some implementations, audio data indicating acoustic characteristics of an utterance is received. A first vector sequence comprising audio features determined from the audio data is generated. A second vector sequence is generated, as output of a first recurrent neural network in response to receiving the first vector sequence as input, where the first recurrent neural network has a transition matrix that implements a cascade of linear operators comprising (i) first linear operators that are complex-valued and unitary, and (ii) one or more second linear operators that are non-unitary. An output vector sequence of a second recurrent neural network is generated. A transcription for the utterance is generated based on the output vector sequence generated by the second recurrent neural network. The transcription for the utterance is provided.
机译:方法,系统和装置,包括在计算机存储介质上编码的计算机程序,用于使用复换传递经常性神经网络进行语音识别。在一些实现中,接收指示话语声学特性的音频数据。生成包括从音频数据确定的音频特征的第一矢量序列。响应于接收到作为输入的第一向量序列的第一传递性神经网络的输出而产生第二向量序列,其中第一复发性神经网络具有实现包括(i)第一线性操作器的级联的过渡矩阵。这是复象和单一的,(ii)一个或多个非统一的第二线性操作员。生成第二反复性神经网络的输出矢量序列。基于由第二经常性神经网络产生的输出矢量序列生成对话语的转录。提供了话语的转录。

著录项

  • 公开/公告号US11069344B2

    专利类型

  • 公开/公告日2021-07-20

    原文格式PDF

  • 申请/专利权人 GOOGLE LLC;

    申请/专利号US201916710005

  • 申请日2019-12-11

  • 分类号G10L15/16;G10L19/02;G10L15/02;G10H1;G06N3/02;G10L17/18;G10L25/30;

  • 国家 US

  • 入库时间 2022-08-24 20:01:00

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