Statistical trajectory models for phonetic recognition
Statistical trajectory models for phonetic recognition
Acoustic and perceptual study of phonetic integration in Spanish voiceless stops
Speech Communication
Heterogeneous acoustic measurements and multiple classifiers for speech recognition
Heterogeneous acoustic measurements and multiple classifiers for speech recognition
Estimation of place of articulation during stop closures of vowel-consonant-vowel utterances
IEEE Transactions on Audio, Speech, and Language Processing
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Unvoiced stops are rapidly varying sounds with acoustic cues to place identity linked to the temporal dynamics. Neurophysiological studies have indicated the importance of joint spectro-temporal processing in the human perception of stops. In this study, two distinct approaches to modeling the spectro-temporal envelope of unvoiced stop phone segments are investigated with a view to obtaining a low-dimensional feature vector for automatic place classification. Classification accuracies on the TIMIT database and a Marathi words dataset show the overall superiority of classifier combination of polynomial surface coefficients and 2D-DCT. A comparison of performance with published results on the place classification of stops revealed that the proposed spectro-temporal feature systems improve upon the best previous systems' performances. The results indicate that joint spectro-temporal features may be usefully incorporated in hierarchical phone classifiers based on diverse class-specific features.