NDLI: On the Use of Forward Temporal Masking and Cumulative Distribution Mapping for Noisy Speech Recognition

Content Provider	IEEE Xplore Digital Library
Author	Choi, E.H.C. Epps, J.
Copyright Year	2005
Description	Author affiliation: Interfaces, Machines & Graphic Environments, Nat. ICT Australia, Sydney, NSW (Choi, E.H.C.; Epps, J.)
Abstract	Robustness in the presence of various types and levels of environmental noise remains an important issue for automatic speech recognition (ASR) systems. This paper describes a new noise-robust ASR front-end that employs a functional model of forward temporal masking combined with cumulative distribution mapping based on MFCC's with c0. Recognition experiments on the Aurora II connected digits database reveal that the proposed front-end achieves an average digit recognition accuracy of 83.24% for a model set trained from clean data and 90.32% for a model set trained from data with multiple noise conditions. Compared with the ETSI standard Mel-cepstral front-end, the proposed front-end obtains a relative error reduction of around 57% for the clean model set and 21% for the multi-condition model set.
Starting Page	1
Ending Page	6
File Size	6924791
Page Count	6
File Format	PDF
ISBN	0780393112
DOI	10.1109/TENCON.2005.301108
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Automatic speech recognition Psychoacoustic models Signal to noise ratio Working environment noise Noise robustness Databases Telecommunication standards Cepstral analysis Speech enhancement
Content Type	Text
Resource Type	Article

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