INTERSPEECH 2006 - ICSLP
This paper compares wavelet and STFT analysis for a speaker-independent stop classification task using the TIMIT database. In the designed experiment the HMM classifier had to assign each test token to one of the following stop classes [d,g,b,t,k,p,dx]. On 6332 stops the wavelet features obtained an overall accuracy of 86% which corresponds to a 14% relative error reduction compared to the STFT baseline system. Furthermore an analysis of the HMM misclassifications revealed that voiced stops were highly confused with their voiceless unaspirated counterparts.
Bibliographic reference. Kühne, Marco / Togneri, Roberto (2006): "Automatic English stop consonants classification using wavelet analysis and hidden Markov models", In INTERSPEECH-2006, paper 1174-Mon3CaP.1.