Please use this identifier to cite or link to this item:
Title: An SNR-incremental stochastic matching algorithm for noisy speech recognition
Authors: Huang, C.-S.
Wang, H.-C.
Lee, C.-H. 
Keywords: Expectation-maximization (EM) algorithm
Robust speech recognition
Stochastic matching
Issue Date: 2001
Citation: Huang, C.-S., Wang, H.-C., Lee, C.-H. (2001). An SNR-incremental stochastic matching algorithm for noisy speech recognition. IEEE Transactions on Speech and Audio Processing 9 (8) : 866-873. ScholarBank@NUS Repository.
Abstract: In this paper, an signal-to-noise ratio (SNR)-incremental stochastic matching (SISM) algorithm is proposed for robust speech recognition in noisy environments. The SISM algorithm is an extension of Sankar and Lee's stochastic matching (SM) for dealing with the distortion due to additive noise. We address two issues concerning the original maximum likelihood-based SM techniques. One concern is that the initial condition of the expectation-maximization (EM) algorithm has to be set carefully if the mismatch between training and testing is large. The other is that the performance is often limited by the newly adapted model in noise compensation instead of reaching the higher level of accuracy often obtained in clean environments. Our proposed SISM algorithm attempts to improve the initial condition and to relax the performance bound. First, the SISM algorithm provides a good initial condition making use of a set of environment-matched models. The second is a recursive operation, i.e., the reference model in each recursion is changed along the direction of SNR increment in order to push the generation performance to that obtained at higher SNR levels. Experimental results show that the SISM algorithm provides further improvement after the best environment-matched performance has been reached, and can therefore obtain an additional discriminative power through using the speech models with higher SNR instead of retraining process.
Source Title: IEEE Transactions on Speech and Audio Processing
ISSN: 10636676
DOI: 10.1109/89.966089
Appears in Collections:Staff Publications

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


checked on Jun 5, 2023


checked on Jun 5, 2023

Page view(s)

checked on May 25, 2023

Google ScholarTM



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