Tuning support vector machines for robust phoneme classification with acoustic waveforms

This work focuses on the robustness of phoneme classification to additive noise in the acoustic waveform domain using support vector machines (SVMs). We address the issue of designing kernels for acoustic waveforms which imitate the state-ofthe-art representations such as PLP and MFCC and are tuned to the physical properties of speech. For comparison, classification results in the PLP representation domain with cepstral mean-and-variance normalization (CMVN) using standard kernels are also reported. It is shown that our custom-designed kernels achieve better classification performance at high noise. Finally, we combine the PLP and acoustic waveform representations to attain better classification than either of the individual representations over the entire range of noise levels tested, from quiet condition up to −18dB SNR.