Towards achieving robust universal neural vocoding

Jaime Lorenzo-Trueba; Thomas Drugman; Javier Latorre; Thomas Merritt; Bartosz Putrycz; Roberto Barra-Chicote; Alexis Moinet; Vatsal Aggarwal

arXiv · 1811.06292

Towards achieving robust universal neural vocoding

Jaime Lorenzo-Trueba·Thomas Drugman·Javier Latorre·Thomas Merritt·Bartosz Putrycz·Roberto Barra-Chicote·Alexis Moinet·Vatsal Aggarwal

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Abstract

This paper explores the potential universality of neural vocoders. We train a WaveRNN-based vocoder on 74 speakers coming from 17 languages. This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker or style seen during training or from an out-of-domain scenario when the recording conditions are studio-quality. When the recordings show significant changes in quality, or when moving towards non-speech vocalizations or singing, the vocoder still significantly outperforms speaker-dependent vocoders, but operates at a lower average relative MUSHRA of 75%. These results are shown to be consistent across languages, regardless of them being seen during training (e.g. English or Japanese) or unseen (e.g. Wolof, Swahili, Ahmaric).

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Jaime Lorenzo-Trueba, Thomas Drugman, Javier Latorre, Thomas Merritt, Bartosz Putrycz, Roberto Barra-Chicote, Alexis Moinet, Vatsal Aggarwal. 2018-11-15. Towards achieving robust universal neural vocoding. https://arxiv.org/abs/1811.06292

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