Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Voice Characteristics Conversion for HMM-based Speech Synthesis System
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Speech synthesis using HMMs with dynamic features
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Adaptation of pitch and spectrum for HMM-based speech synthesis using MLLR
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
The Nitech-NAIST HMM-Based Speech Synthesis System for the Blizzard Challenge 2006
IEICE - Transactions on Information and Systems
A Fully Consistent Hidden Semi-Markov Model-Based Speech Recognition System
IEICE - Transactions on Information and Systems
Review: Statistical parametric speech synthesis
Speech Communication
Robust speaker-adaptive HMM-based text-to-speech synthesis
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Brief communication: Computation of mutual information from Hidden Markov Models
Computational Biology and Chemistry
Analysis and HMM-based synthesis of hypo and hyperarticulated speech
Computer Speech and Language
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A statistical speech synthesis system based on the hidden Markov model (HMM) was recently proposed. In this system, spectrum, excitation, and duration of speech are modeled simultaneously by context-dependent HMMs, and speech parameter vector sequences are generated from the HMMs themselves. This system defines a speech synthesis problem in a generative model framework and solves it based on the maximum likelihood (ML) criterion. However, there is an inconsistency: although state duration probability density functions (PDFs) are explicitly used in the synthesis part of the system, they have not been incorporated into its training part. This inconsistency can make the synthesized speech sound less natural. In this paper, we propose a statistical speech synthesis system based on a hidden semi-Markov model (HSMM), which can be viewed as an HMM with explicit state duration PDFs. The use of HSMMs can solve the above inconsistency because we can incorporate the state duration PDFs explicitly into both the synthesis and the training parts of the system. Subjective listening test results show that use of HSMMs improves the reported naturalness of synthesized speech.