Exploiting phonetic and phonological similarities as a first step for robust speech recognition

This paper presents two speech recognition systems which use the notion of phonetic and phonological similarity to improve the robustness of phoneme recognition. The first recognition system, YASPER, uses phonetic feature extraction engines to identify phonemes based on overlap relations between phonetic features. The second system uses the CMU Sphinx 3.7 decoder based on statistical context-dependent phone models. Experiments have been carried out on the TIMIT corpus which show improvements in phoneme error rate when a projection set constructed with respect to phonetic and phonological similarity is used. It is envisaged that in future, the two systems will provide alternative parallel streams of hypotheses for each interval of the speech signal and will work together as experts in the phoneme recognition process.