Speaker-independent raw waveform model for glottal excitation
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A4 Artikkeli konferenssijulkaisussa
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Date
2018-09-02
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Mcode
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Language
en
Pages
2012-2016
Series
Proceedings of Interspeech, Interspeech - Annual Conference of the International Speech Communication Association
Abstract
Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i.e., generating speech waveforms from acoustic features. These models have been shown to improve the generated speech quality over classical vocoders in many tasks, such as text-to-speech synthesis and voice conversion. Furthermore, conditioning WaveNets with acoustic features allows sharing the waveform generator model across multiple speakers without additional speaker codes. However, multi-speaker WaveNet models require large amounts of training data and computation to cover the entire acoustic space. This paper proposes leveraging the source-filter model of speech production to more effectively train a speaker-independent waveform generator with limited resources. We present a multi-speaker ’GlotNet’ vocoder, which utilizes a WaveNet to generate glottal excitation waveforms, which are then used to excite the corresponding vocal tract filter to produce speech. Listening tests show that the proposed model performs favourably to a direct WaveNet vocoder trained with the same model architecture and data.Description
Keywords
Glottal source generation, WaveNet, mixture density network
Other note
Citation
Juvela, L, Tsiaras, V, Bollepalli, B, Airaksinen, M, Yamagishi, J & Alku, P 2018, Speaker-independent raw waveform model for glottal excitation . in Proceedings of Interspeech . Interspeech - Annual Conference of the International Speech Communication Association, International Speech Communication Association (ISCA), pp. 2012-2016, Interspeech, Hyderabad, India, 02/09/2018 . https://doi.org/10.21437/Interspeech.2018-1635