Phoneme Classification in Frequency Subbands using Ensemble Methods

Phoneme classification in frequency bands of acoustic waveforms is studied. The goal is to investigate whether separate classifications across a number of subband signals, combined using appropriate machine learning algorithms, can provide performance similar to classification performed directly on the original acoustic waveforms. If this is the case, then combining subband classifications might lead to speech recognition algorithms that are robust to linear filtering and narrow-band noise. We perform proof-of-concept experiments on three binary phoneme classification tasks of varying difficulty, using Support Vector Machine subband classifiers which are combined by simple and weighted voting techniques as well as stacked generalization methods. We find that combining subband classifiers improves performance and that the improvement becomes more marked as the number of subbands increases.