Title

Joint Frequency Domain and Reconstructed Phase Space Features for Speech Recognition

Document Type

Conference Proceeding

Language

eng

Format of Original

4 p.

Publication Date

5-17-2004

Publisher

Institute of Electrical and Electronics Engineers

Source Publication

Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004 (ICASSP '04)

Source ISSN

1520-6149

Original Item ID

doi: 10.1109/ICASSP.2004.1326040

Abstract

A novel method for speech recognition is presented, utilizing nonlinear/chaotic signal processing techniques to extract time-domain based, reconstructed phase space features. This work examines the incorporation of trajectory information into this model as well as the combination of both MFCC and RPS feature sets into one joint feature vector. The results demonstrate that integration of trajectory information increases the recognition accuracy of the typical RPS feature set, and when MFCC and RPS feature sets are combined, improvement is made over the baseline. This result suggests that the features extracted using these nonlinear techniques contain different discriminatory information than the features extracted from linear approaches alone.

Comments

Published as part of the proceedings of the conference, IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol. 1: 533-536. DOI.