Taylor Series Expansion of Psychoacoustic Corruption Function for Noise Robust Speech Recognition

被引：0

作者：

Das, Biswajit ^{[1
]}

Panda, Ashish ^{[1
]}

机构：

[1] TCS Innovat Labs, Mumbai Yantra Pk, Thana 400601, Maharashtra, India

来源：

PROCEEDINGS OF 2016 IEEE 13TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP 2016) | 2016年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we present model domain adaptation algorithms for noise robust speech recognition. We have proposed a Taylor Series expansion of the psychoacoustic corruption function, which provides for superior noise robustness. The proposed joint adaptation algorithm consists of Psychoacoustic Model Compensation (Psy-Comp) and Model-domain Cepstral Mean Normalization (MCMN). While the Psy-Comp compensates for the additive noise component, the MCMN compensates for the channel component. The proposed algorithms are validated through experiments on noise corrupted TIMIT speech recognition database. We show that the joint compensation scheme provides 5% (absolute) performance gain compared to the Vector Taylor Series (VTS) scheme. Also, the proposed technique is computationally efficient and faster as compared to the VTS scheme. Keywords: Speech Recognition, Additive Noise, Model Compensation, Model Domain Mean Normalization, Vector Taylor Series

引用

页码：568 / 572

页数：5

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