Document Type




Format of Original

9 p.

Publication Date



Institute of Electrical and Electronics Engineers

Source Publication

IEEE Transactions on Speech and Audio Processing

Source ISSN


Original Item ID

doi: 10.1109/TSA.2005.848885


This paper introduces a novel time-domain approach to modeling and classifying speech phoneme waveforms. The approach is based on statistical models of reconstructed phase spaces, which offer significant theoretical benefits as representations that are known to be topologically equivalent to the state dynamics of the underlying production system. The lag and dimension parameters of the reconstruction process for speech are examined in detail, comparing common estimation heuristics for these parameters with corresponding maximum likelihood recognition accuracy over the TIMIT data set. Overall accuracies are compared with a Mel-frequency cepstral baseline system across five different phonetic classes within TIMIT, and a composite classifier using both cepstral and phase space features is developed. Results indicate that although the accuracy of the phase space approach by itself is still currently below that of baseline cepstral methods, a combined approach is capable of increasing speaker independent phoneme accuracy.


Accepted version. IEEE Transactions on Speech and Audio Processing, Vol. 13, No. 4 (July 2005): 458-466. DOI. © 2005 Institute of Electrical and Electronics Engineers (IEEE). Used with permission.