Dynamic spectral shape features for speaker-independent automatic recognition of stop consonants

Several acoustic feature sets and automatic classifiers were investigated to determine a combination of features and classifiers which would permit accurate bottom-up speaker- and vowel-independent automatic recognition of initial stop consonants in English. The features evaluated included a form of cepstral coefficients and formants, each computed both for one static frame and as spectral trajectories over various segments of the speech signal. The classifiers investigated included Bayesian maximum-likelihood (BML), artificial neural network (NN), and K-nearest-neighbor (KNN) classifiers. The most accurate results, over 93% of the six stops correctly identified with a speaker-independent classifier, were obtained with the BML classifier using cepstral coefficient trajectories as a 20-dimensional feature vector. These results for stop recognition are higher than any results previously reported for a database of similar diversity