A Hidden Semi-Markov Model-Based Speech Synthesis System
IEICE - Transactions on Information and Systems
The Nitech-NAIST HMM-Based Speech Synthesis System for the Blizzard Challenge 2006
IEICE - Transactions on Information and Systems
An Improvement of HSMM-Based Speech Synthesis by Duration-Dependent State Transition Probabilities
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Review: Statistical parametric speech synthesis
Speech Communication
Robust speaker-adaptive HMM-based text-to-speech synthesis
IEEE Transactions on Audio, Speech, and Language Processing
Some aspects of ASR transcription based unsupervised speaker adaptation for HMM speech synthesis
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Analysis and HMM-based synthesis of hypo and hyperarticulated speech
Computer Speech and Language
Structural Bayesian Linear Regression for Hidden Markov Models
Journal of Signal Processing Systems
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Describes a technique for synthesizing speech with arbitrary speaker characteristics using speaker independent speech units, which we call "average voice" units. The technique is based on an HMM-based text-to-speech (TTS) system and maximum likelihood linear regression (MLLR) adaptation algorithm. In the HMM-based TTS system, speech synthesis units are modeled by multi-space probability distribution (MSD) HMMs which can model spectrum and pitch simultaneously in a unified framework. We derive an extension of the MLLR algorithm to apply it to MSD-HMMs. We demonstrate that a few sentences uttered by a target speaker are sufficient to adapt not only voice characteristics but also prosodic features. Synthetic speech generated from adapted models using only four sentences is very close to that from speaker dependent models trained using 450 sentences.