Abstract
A novel type of hidden Markov model (HMM) has been developed to account explicitly for the context-dependent vowel acoustic transitions in consonant-vowel and vowel consonant phonetic environments. The major difference between this type of HMM and the standard Gaussian HMM is that the Gaussian mean vectors associated with the vowel HMM states, which are intended to model the vowel acoustic transitions, are set to be linearly interpolated values between those of the vowel steady state and those of the assumed locus for the adjacent consonant. The locus vectors, one for each consonant except for /h/, are trained together with all other HMM parameters using the Baum-Welch algorithm. The training procedure is fully automatic and converges to a local maximum. The model is incorporated in a phonetically based 75000-word vocabulary speech recognizer and provides a modest improvement in recognition rate over the standard approach. >
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
