TRAJECTORY ANALYSIS OF SPEECH USING CONTINUOUS STATE HIDDEN MARKOV MODELS

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

Abstract

Many current speech models used in recognition involve thousands of parameters, whereas the mechanisms of speech production are conceptually very simple. We present and evaluate a new continuous state probabilistic model (CS-HMM) for recovering dwell-transition and phoneme sequences from dynamic speech production features. We show that with very few parameters, these features can be tracked, and phoneme sequences recovered, with promising accuracy.

Details

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
Publication statusPublished - 2014
EventICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Florence, Italy, United Kingdom
Duration: 4 May 20149 May 2014

Conference

ConferenceICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CountryUnited Kingdom
Period4/05/149/05/14