Document Type

Conference Proceeding



Format of Original

4 p.

Publication Date



Institute of Electrical and Electronics Engineers

Source Publication

Acoustics, Speech, and Signal Processing, 2003

Source ISSN


Original Item ID

doi: 10.1109/ICASSP.2003.1198716


The paper presents a novel method for speech recognition by utilizing nonlinear/chaotic signal processing techniques to extract time-domain based phase space features. By exploiting the theoretical results derived in nonlinear dynamics, a processing space called a reconstructed phase space can be generated where a salient model (the natural distribution of the attractor) can be extracted for speech recognition. To discover the discriminatory power of these features, isolated phoneme classification experiments were performed using the TIMIT corpus and compared to a baseline classifier that uses MFCC (Mel frequency cepstral coefficient) features. The results demonstrate that phase space features contain substantial discriminatory power, even though MFCC features outperformed the phase space features on direct comparisons. The authors conjecture that phase space and MFCC features used in combination within a classifier may yield increased accuracy for various speech recognition tasks.


Accepted version. Published as part of the proceedings of the conference, Acoustics, Speech, and Signal Processing, 2003, Vol. 1: 60-63. DOI. © 2003 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.

johnson_7811acc.docx (441 kB)
ADA Accessible Version