title: Automatic speech recognition using articulatory features from subject-independent acoustic-to-articulatory inversion.
abstract: An automatic speech recognition approach is presented which uses articulatory features estimated by a subject-independent acoustic-to-articulatory inversion. The inversion allows estimation of articulatory features from any talker's speech acoustics using only an exemplary subject's articulatory-to-acoustic map. Results are reported on a broad class phonetic classification experiment on speech from English talkers using data from three distinct English talkers as exemplars for inversion. Results indicate that the inclusion of the articulatory information improves classification accuracy but the improvement is more significant when the speaking style of the exemplar and the talker are matched compared to when they are mismatched.
authors.affiliation: Signal Analysis and Interpretation Laboratory, Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089, USA. prasantg@usc.edu
authors.forename: Prasanta Kumar
authors.initials: PK
authors.lastname: Ghosh
authors.forename: Shrikanth
authors.initials: S
authors.lastname: Narayanan
mesh_terms: D001331:Automation; D005260:Female; D006801:Humans; D007568:Jaw / Q000033:anatomy & histology / Q000502:physiology*; D016014:Linear Models; D008297:Male; D008962:Models, Theoretical; D009055:Mouth / Q000033:anatomy & histology / Q000502:physiology*; D010699:Phonation; D010700:Phonetics; D013061:Speech Acoustics; D013068:Speech Production Measurement; D049250:Speech Recognition Software