Utterance-Wise Recurrent Dropout And Iterative Speaker Adaptation For Robust Monaural Speech Recogni

Posted jarvanwang

tags:

篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了Utterance-Wise Recurrent Dropout And Iterative Speaker Adaptation For Robust Monaural Speech Recogni相关的知识,希望对你有一定的参考价值。

单声道语音识别的逐句循环Dropout迭代说话人自适应

   

WRBNwide residual BLSTM network,宽残差双向长短时记忆网络)

[2] J. Heymann, L. Drude, and R. Haeb-Umbach, "Wide residual blstm network with discriminative speaker adaptation for robust speech recognition," submitted to the CHiME, vol. 4, 2016.

reverberationn. [] 混响;反射;反响;回响

   

CLDNNconvolutional, long short-term memory, fully connected deep neural networks,卷积-长短时记忆-全连接深度神经网络)

[1] T.N. Sainath, O. Vinyals, A. Senior, and H. Sak, "Convolutional, long short-term memory, fully connected deep neural networks," in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. IEEE, 2015, pp. 45804584.

   

speech separation,语音分离,将多说话人同时说话的语句分离为各个说话人独立说话的语句。

   

LSTM训练中使用Dropout能有效缓解过拟合。

[3] G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. Salakhutdinov, "Improving neural networks by preventing co-adaptation of feature detectors," arXiv preprint arXiv:1207.0580, 2012.

   

在输出门、遗忘门以及输入门使用基于语句采样丢帧Mask能取得最优结果(Cheng dropout)。

[7] G. Cheng, V. Peddinti, D. Povey, V. Manohar, S. Khudanpur, and Y. Yan, "An exploration of dropout with lstms," in Proceedings of Interspeech, 2017.

   

基于MLLR的迭代自适应方法,使用上一次迭代的解码结果来更新高斯参数。

[10] P.C. Woodland, D. Pye, and M.J.F. Gales, "Iterative unsupervised adaptation using maximum likelihood linear regression," inSpokenLanguage, 1996.ICSLP96.Proceedings., Fourth International Conference on. IEEE, 1996, vol. 2, pp. 1133–1136.

   

   

近期提出了一种batch正则化说话人自适应。

[14] P. Swietojanski, J. Li, and S. Renals, "Learning hidden unit contributions for unsupervised acoustic model adaptation," IEEE/ACMTransactionsonAudio,Speech, and Language Processing, vol. 24, no. 8, pp. 1450– 1463, 2016.

   

本文使用了无监督的LIN说话人自适应

[11]

使用的LIN层矩阵维数为80*80,该层被三个输入特征共享(原始、deltadelta-delta)。

   

本文尝试使用以下两种方式进行迭代的说话人自适应:

  • 在迭代时使用上一次迭代的模型生成新标签进行训练。
  • 每次迭代堆叠一个额外的线性输入层(数学上,多个线性层相当于一个隐层)

   

传统DNN训练方式是segment-wise

   

实验得出,使用RNN时,Iter(迭代方案)更优;使用tri-gram时,Stack(堆叠)方案更优

以上是关于Utterance-Wise Recurrent Dropout And Iterative Speaker Adaptation For Robust Monaural Speech Recogni的主要内容,如果未能解决你的问题,请参考以下文章

poj 3708 Recurrent Function

《RECURRENT BATCH NORMALIZATION》

Simple Recurrent Unit,单循环单元

在 colab 崩溃模型中不使用recurrent_dropout?

论文笔记之: Recurrent Models of Visual Attention

论文笔记之:RATM: RECURRENT ATTENTIVE TRACKING MODEL