Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICASSP.2013.6639097
Title: Approximated Parallel Model Combination for efficient noise-robust speech recognition
Authors: Sim, K.C. 
Keywords: Noise robust speech recognition
parallel model combination
trajectory-based compensation
vector Taylor series
Issue Date: 18-Oct-2013
Citation: Sim, K.C. (2013-10-18). Approximated Parallel Model Combination for efficient noise-robust speech recognition. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings : 7383-7387. ScholarBank@NUS Repository. https://doi.org/10.1109/ICASSP.2013.6639097
Abstract: Parallel Model Combination (PMC) and Vector Taylor Series (VTS) are two model-based approaches for noise-robust speech recognition. The latter is more popular because of its simple compensation formulae for both the static and dynamic parameters. Furthermore, this VTS compensation formulation can be easily extended to noise adaptive training where the parameters of the underlying pseudo-clean speech and distortion models can be optimized. PMC lacks the above benefits because of its nonlinear variance compensation formula. In this paper, the Approximated PMC (APMC) method is proposed where linearized PMC variance compensation is used. The same approximation has also been applied to Trajectory-based APMC (TAPMC) to achieve a four-time computational saving over the Trajectory-based PMC (TPMC). The dynamic parameter compensation and noise re-estimation formulae for APMC are also derived. Experimental results on AURORA 4 show that APMC and TAPMC consistently outperformed the standard VTS and Trajectory-based VTS (TVTS) by 6.3% and 5.3% relative respectively. © 2013 IEEE.
Source Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/78023
ISBN: 9781479903566
ISSN: 15206149
DOI: 10.1109/ICASSP.2013.6639097
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

3
checked on Oct 14, 2018

Page view(s)

27
checked on Jun 29, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.