Feature representations and classification procedures for Slovene phoneme recognition

Abstract

In this paper the comparison of performances of different feature representations of the speech signal and comparison of classification procedures for Slovene phoneme recognition are presented. Recognition results are obtained on the database of continuous Slovene speech consisting of short Slovene sentences spoken by female speakers. MEL-cepstrum and LPC-cepstrum features combined with the normalized frame loudness were found to be the most suitable feature representations for Slovene speech. It was found that determination of MEL-cepstrum using linear spacing of bandpass filters gave significantly better results for speaker dependent recognition. Comparison of classification procedures favours the Bayes classification assuming normal distribution of the feature vectors (BNF) to the classification based on quadratic discriminant functions (DF) for minimum mean-square error and subspace method (SM), which does not confirm the results obtained in some previous studies for German and Finn speech. Additionally, classification procedures based on hidden Markov models (HMM) and the Kohonen Self-Organizing Map (KSOM) were tested on a smaller amount of speech data (1 speaker only). Classification results are comparable with classification using BNF.