Hierarchical phoneme discrimination by hidden Markov modelling using cepstrum and formant information

A report is presented of comparative results for vowel classification using hidden Markov models based on linear predictive coding (LPC)-based cepstral vectors and formant features. The classification accuracy is shown to be significantly improved by using time duration constraints in formant feature space, especially for the formant mel-frequency representation and its time derivative. The highest vowel recognition accuracy is obtained by integrating the two feature spaces, multiplying the probabilities computed in the separate feature spaces. This improvement of vowel recognition is extended to the more general phoneme recognition task by use of a hierarchical feature integration method, which utilizes the vowel recognition results in formant feature space together with consonant recognition based on the LPC-based cepstral feature space