Automatic English stop consonants classification using wavelet analysis and hidden Markov models
نویسندگان
چکیده
This paper compares wavelet and STFT analysis for a speakerindependent 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.
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