Synthesis of Child Speech With HMM Adaptation and Voice Conversion

Oliver Watts, Junichi Yamagishi, Simon King, Kay Berkling

Research output: Contribution to journalArticlepeer-review

Abstract

The synthesis of child speech presents challenges both in the collection of data and in the building of a synthesizer from that data. We chose to build a statistical parametric synthesizer using the hidden Markov model (HMM)-based system HTS, as this technique has previously been shown to perform well for limited amounts of data, and for data collected under imperfect conditions. Six different configurations of the synthesizer were compared, using both speaker-dependent and speaker-adaptive modeling techniques, and using varying amounts of data. For comparison with HMM adaptation, techniques from voice conversion were used to transform existing synthesizers to the characteristics of the target speaker. Speaker-adaptive voices generally outperformed child speaker-dependent voices in the evaluation. HMM adaptation outperformed voice conversion style techniques when using the full target speaker corpus; with fewer adaptation data, however, no significant listener preference for either HMM adaptation or voice conversion methods was found.
Original languageEnglish
Pages (from-to)1005-1016
Number of pages12
JournalIEEE Transactions on Audio, Speech and Language Processing
Volume18
Issue number5
DOIs
Publication statusPublished - Jul 2010

Keywords

  • hidden Markov models
  • speech synthesis

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