By Topic

Robust Feature Extraction Using Spectral Peaks of the Filtered Higher Lag Autocorrelation Sequence of the Speech Signal

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

4 Author(s)
Farahani, G. ; Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran ; Ahadi, S.M. ; Homayounpour, M.M. ; Kashi, A.

This paper presents a new feature set for noisy speech recognition in autocorrelation domain. The autocorrelation domain is well-known for its pole preserving and noise separation properties. Therefore, in this paper we use the autocorrelation domain as an appropriate candidate for robust feature extraction. In our approach, initially, the lower lags of the noisy speech autocorrelation sequence are discarded and then, the effect of noise is further suppressed using a high pass filter in autocorrelation domain. Finally, the speech feature set is found using the spectral peaks of this filtered autocorrelation sequence We tested our features on the Aurora 2 noisy isolated-word task and found that it led to noticeable improvements over other autocorrelation-based and differential spectral-based methods implemented previously

Published in:

Signal Processing and Information Technology, 2006 IEEE International Symposium on

Date of Conference:

Aug. 2006