Abstract
Multiple emotional voice conversion in Vietnamese HMM-based speech synthesis using non-negative matrix factorization
Highlights
In many practical applications, TTS with multiple synthesized emotional voices is required while the requirement of having huge amounts data of emotional target voices for training is usually not available
State-of-the-art voice conversion (VC) still cannot synthesize target speech while keeping the detail information related to speaker emotions of the target voice
We proposed to use the exemplar-based VC using non-negative matrix factorization combined with Hidden Markov Model (HMM)-based TTS to synthesize multiple emotional voices that can keep the detail information related to speaker emotions
Summary
TTS with multiple synthesized emotional voices is required while the requirement of having huge amounts data of emotional target voices for training is usually not available. In this approach, synthesized neutral speech is adapted to target emotional voices with a few amounts of emotional target data. In both HMM-based synthesis and voice adaption, the structures of the estimated spectrum correspond to the average of different speech spectra in the training database due to the use of the mean vector. Using a VC method as a post-processing step for HMM-based TTS is another approach to synthesize multiple emotional target voices. We proposed to use the exemplar-based VC using non-negative matrix factorization combined with HMM-based TTS to synthesize multiple emotional voices that can keep the detail information related to speaker emotions.
Sign-in/Register to view highlights & summary 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: International Journal of ADVANCED AND APPLIED SCIENCES
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.
