Classification of coarse phonetic categories in continuous speech: statistical classifiers vs. temporal flow connectionist network

A comparison of the temporal flow model (TFM) as a connectionist approach with statistical methods like the hidden Markov model (HMM) and the maximum-likelihood (ML) classifier on the basis of frame and segment recognition experiments is presented. All three methods were applied to a coarse phonetic classification task in a speaker-dependent mode. The seven coarse phonetic categories (CPCs) used correspond to the categories of manner of articulation. The experiments were performed on manually labeled continuous-speech data incorporating two versions of 50 phonetically balanced sentences. A short time cepstral representation of the speech data was chosen as the basis for all classification experiments. The best results were achieved with a context-dependent HMM. Experiments without the use of segment context noticeably yield better overall results for the TFM. Both are found to be superior to the ML classifier