Abstract
Phoneme Hidden Markov Model (HMM) are generally evaluated in terms of the phoneme recognition rate by using speech data extracted based on phoneme labels. This paper proposes an evaluation method that does not use phoneme labels for extraction. Consequently, phoneme HMMs can be evaluated even if a speech database without phoneme labeling is used. In this study, concatenation training of the phoneme HMMs is executed using a large-scale speaker-independent continuous-speech database. Evaluation of the HMM phoneme recognition rate which is a function of the number of training speakers, using the proposed evaluation method demonstrates its effectiveness.
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 callMore From: Electronics and Communications in Japan (Part III: Fundamental Electronic Science)
Disclaimer: 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.
