Work

Speech Synthesis Based on Hidden Markov Models

Year: 2013

Type: article

Abstract: 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.

Source: Proceedings of the IEEE

DOI: 10.1109/jproc.2013.2251852

Authors: Keiichi Tokuda, Yoshihiko Nankaku, Tomoki Toda, Heiga Zen, Junichi Yamagishi, Keiichiro Oura

Institutions: Nagoya Institute of Technology, University of Edinburgh

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Engineering and Physical Sciences Research Council, Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology

Awards: Deep architectures for statistical speech synthesis, aaa, Natural Speech Technology