Cross-lingual Text-To-Speech with Flow-based Voice Conversion for Improved Pronunciation

By Nikolaos Ellinas, Georgios Vamvoukakis, Konstantinos Markopoulos, Georgia Maniati, Panos Kakoulidis, June Sig Sung, Inchul Hwang, Spyros N. Raptis, Aimilios Chalamandaris, Pirros Tsiakoulis. 2022

3 citations recorded by the source corpus.

This paper presents a method for end-to-end cross-lingual text-to-speech (TTS) which aims to preserve the target language's pronunciation regardless of the original speaker's language. The model used is based on a non-attentive Tacotron architecture, where the decoder has been replaced with a normalizing flow network conditioned on the speaker identity, allowing both TTS and voice conversion (VC) to be performed by the same model due to the inherent linguistic content and speaker identity disentanglement. When used in a cross-lingual setting, acoustic features are initially produced with a native speaker of the target language and then voice conversion is applied by the same model in order to convert these features to the target speaker's voice. We verify through objective and subjective evaluations that our method can have benefits compared to baseline cross-lingual synthesis. By including speakers averaging 7.5 minutes of speech, we also present positive results on low-resource scenarios.

Subfields: Artificial Intelligence, Signal Processing

Citations in the corpus

• Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling (2023, neutral): “Previous methods to tackle these challenges typically augment end-to-end TTS models with specific subnets for speaker and language control [Nachmani and Wolf, 2019, Zhang et al., 2020, Yang and He, 2020, Ellinas et al., 2022, Cai et al., 2023].”