ASR:2015-04-27

Speech Processing

AM development

Environment

• grid-11 often shut down automatically, too slow computation speed.
• New grid-13 added, using gpu970
• To update the wiki enviroment infomation

RNN AM

• details at http://liuc.cslt.org/pages/rnnam.html
• Test monophone on RNN using dark-knowledge
• run using wsj,MPE

Mic-Array

• Change the prediction from fbank to spectrum features
• investigate alpha parameter in time domian and frquency domain
• ALPHA>=0, using data generated by reverber toolkit
• consider theta

RNN-DAE(Deep based Auto-Encode-RNN)

• HOLD --Zhiyong Zhang
• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=261

Speaker ID

• DNN-based sid --Yiye Lin
• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zhangzy&step=view_request&cvssid=327

Ivector&Dvector based ASR

• Cluster the speakers to speaker-classes, then using the distance or the posterior-probability as the metric
• Direct using the dark-knowledge strategy to do the ivector training.
• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?step=view_request&cvssid=340
• Ivector dimention is smaller, performance is better
• Augument to hidden layer is better than input layer
• train on wsj(testbase dev93+evl92)

Dark knowledge

• Ensemble

• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=264 --Zhiyong Zhang

• adaptation for chinglish under investigation --Mengyuan Zhao

• Try to improve the chinglish performance extremly

• unsupervised training with wsj contributes to aurora4 model --Xiangyu Zeng

• test large database with AMIDA

bilingual recognition

• http://cslt.riit.tsinghua.edu.cn/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=359 --Zhiyuan Tang