Speech Synthesis Based on Hidden Markov Models

This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech. The main advantage of this approach is its flexibility in changing speaker identities, emotions, and speaking styles. This paper also discusses the relation between the HMM-based approach and the more conventional unit-selection approach that has dominated over the last decades. Finally, advanced techniques for future developments are described.

I. Introduction

Text-to-speech (TTS) synthesis is a technique for generating intelligible, natural-sounding artificial speech for a given input text. It has been used widely in various applications including in-car navigation systems, e-book readers, voice-over functions for the visually impaired, and communication aids for the speech impaired. More recent applications include spoken dialog systems, communicative robots, singing speech synthesizers, and speech-to-speech translation systems.