GlotNet-A Raw Waveform Model for the Glottal Excitation in Statistical Parametric Speech Synthesis

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Crete

Abstract

Recently, generative neural network models which operate directly on raw audio, such as WaveNet, have improved the state of the art in text-to-speech synthesis (TTS). Moreover, there is increasing interest in using these models as statistical vocoders for generating speech waveforms from various acoustic features. However, there is also a need to reduce the model complexity, without compromising the synthesis quality. Previously, glottal pulseforms (i.e., time-domain waveforms corresponding to the source of human voice production mechanism) have been successfully synthesized in TTS by glottal vocoders using straightforward deep feedforward neural networks. Therefore, it is natural to extend the glottal waveform modeling domain to use the more powerful WaveNet-like architecture. Furthermore, due to their inherent simplicity, glottal excitation waveforms permit scaling down the waveform generator architecture. In this study, we present a raw waveform glottal excitation model, called GlotNet, and compare its performance with the corresponding direct speech waveform model, WaveNet, using equivalent architectures. The models are evaluated as part of a statistical parametric TTS system. Listening test results show that both approaches are rated highly in voice similarity to the target speaker, and obtain similar quality ratings with large models. Furthermore, when the model size is reduced, the quality degradation is less severe for GlotNet.

Details

Original languageEnglish
Article number8675543
Pages (from-to)1019-1030
Number of pages12
JournalIEEE/ACM Transactions on Audio, Speech, and Language Processing
Volume27
Issue number6
Early online date2019
Publication statusPublished - 1 Jun 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Acoustics, Vocoders, Speech synthesis, Computational modeling, Hidden Markov models, Neural networks, Glottal source model, text-to-speech, WaveNet

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