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Complex evolution recurrent neural networks

机译:复杂进化递归神经网络

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,用于使用复杂进化递归神经网络进行语音识别。在一些实施方式中,接收指示话语的声学特性的音频数据。产生包括从音频数据确定的音频特征的第一矢量序列。响应于接收到第一矢量序列作为输入而生成第二矢量序列,作为第一递归神经网络的输出,其中第一递归神经网络具有实现实现包括(i)第一线性算子的线性算子级联的转换矩阵。它们是复数且是unit的,以及(ii)一个或多个非-的第二线性算子。产生第二递归神经网络的输出向量序列。基于第二递归神经网络生成的输出矢量序列,生成话语的转录。提供话语的转录。

著录项

  • 公开/公告号US10529320B2

    专利类型

  • 公开/公告日2020-01-07

    原文格式PDF

  • 申请/专利权人 GOOGLE LLC;

    申请/专利号US201916251430

  • 申请日2019-01-18

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

  • 国家 US

  • 入库时间 2022-08-21 11:19:01

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