Signal modeling enhancements for automatic speech recognition

Experiments in modeling speech signals for phoneme classification are described. Enhancements to standard speech processing methods include basis vector representations of dynamic feature trajectories, morphological smoothing (dilation) of spectral features, and the use of many closely spaced, short analysis windows. Results are reported from experiments using the TIMIT database of up to 71.0% correct classification of 16 presegmented vowels in a noise-free environment, and 54.5% correct classification in a 10 dB signal-to-noise ratio environment.