Abstract
This paper presents a method of feature extraction for the automatic recognition of voiceless unaspirated stop consonants in Mandarin speech. The features are derived from the spectrographic acoustic patterns of syllable-initial voiceless unaspirated stops /p,t,k/, which include the burst spectrum, the formant transition, and the voice onset time. A normalization process for the second and the third formants at the voice onset is proposed. Based on these derived features, Bayes classifiers and a layered neural net are applied to classify the places of articulation of these stop consonants. The experiments show that the derived features are robust and efficient for speaker-independent speech recognition, and the neural net is a preferable choice in the classification of these stops in multiple contexts.
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