By Topic

High-performance robust speech recognition using stereo training data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Li Deng ; Microsoft Res., Redmond, WA, USA ; Acero, A. ; Li Jiang ; Droppo, J.
more authors

We describe a novel technique of SPLICE (Stereo-based Piecewise Linear Compensation for Environments) for high performance robust speech recognition. It is an efficient noise reduction and channel distortion compensation technique that makes effective use of stereo training data. We present a new version of SPLICE using the minimum-mean-square-error decision, and describe an extension by training clusters of hidden Markov models (HMMs) with SPLICE processing. Comprehensive results using a Wall Street Journal large vocabulary recognition task and with a wide range of noise types demonstrate the superior performance of the SPLICE technique over that under noisy matched conditions (19% word error rate reduction). The new technique is also shown to consistently outperform the spectral-subtraction noise reduction technique, and is currently being integrated into the Microsoft MiPad, a new generation PDA prototype

Published in:

Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on  (Volume:1 )

Date of Conference:

2001