Improving the expressiveness of neural vocoding with non-affine Normalizing Flows
| Autor: | Daniel Korzekwa, Yunlong Jiao, Adam Gabrys, Roberto Barra-Chicote, Viacheslav Klimkov |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Theoretical computer science Computer Science - Computation and Language Computer science Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Affine transformation Computation and Language (cs.CL) Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing |
| Popis: | This paper proposes a general enhancement to the Normalizing Flows (NF) used in neural vocoding. As a case study, we improve expressive speech vocoding with a revamped Parallel Wavenet (PW). Specifically, we propose to extend the affine transformation of PW to the more expressive invertible non-affine function. The greater expressiveness of the improved PW leads to better-perceived signal quality and naturalness in the waveform reconstruction and text-to-speech (TTS) tasks. We evaluate the model across different speaking styles on a multi-speaker, multi-lingual dataset. In the waveform reconstruction task, the proposed model closes the naturalness and signal quality gap from the original PW to recordings by $10\%$, and from other state-of-the-art neural vocoding systems by more than $60\%$. We also demonstrate improvements in objective metrics on the evaluation test set with L2 Spectral Distance and Cross-Entropy reduced by $3\%$ and $6\unicode{x2030}$ comparing to the affine PW. Furthermore, we extend the probability density distillation procedure proposed by the original PW paper, so that it works with any non-affine invertible and differentiable function. Accepted to Interspeech 2021, 5 pages,3 figures |
| Databáze: | OpenAIRE |
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