Techniques for improved stop and nasal consonant discrimination

The automatic recognition of stop and nasal consonants is known to be a difficult recognition task. This paper presents various techniques that can be used to improve the discrimination of stop and nasal consonants. An improved spectral representation for stop consonants is proposed, which unlike other feature representations, emphasizes the mid‐to‐high regions of the spectrum. A subspace projection approach, which is used as a preprocessing step in a hidden Markov model based system, is also proposed for improved nasal discrimination. This approach finds a transformation matrix which maps the original nasal observation onto a subspace such that the ‘‘distance’’ between the nasals is maximized on the subspace. Two statistical distance measures are investigated for finding the transformation matrix, namely the divergence and the Bhattacharyya measures. Results on stop and nasal consonant recognition will be presented using the subspace approach and the improved spectral stop representation.