By Topic

Temporal Structure Normalization of Speech Feature for Robust Speech Recognition

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
$33 $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

3 Author(s)
Xiong Xiao ; Nanyang Technol. Univ., Nanyang ; Eng Siong Chng ; Haizhou Li

This letter presents a new feature normalization technique to normalize the temporal structure of speech features. The temporal structure of the features is partially represented by its power spectral density (PSD). We observed that the PSD of the features varies with the corrupting noise and signal-to-noise ratio. To reduce the PSD variation due to noise, we propose to normalize the PSD of features to a reference function by filtering the features. Experimental results on the AURORA-2 task show that the proposed approach when combined with the mean and variance normalization improves the speech recognition accuracy significantly; the system achieves 69.11% relative error rate reduction over the baseline.

Published in:

IEEE Signal Processing Letters  (Volume:14 ,  Issue: 7 )