Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data

Jarod Duret; Titouan Parcollet; Yannick Estève

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arXiv · 2306.17199

Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data

Jarod Duret·Titouan Parcollet·Yannick Estève

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Abstract

We propose a method for speech-to-speech emotionpreserving translation that operates at the level of discrete speech units. Our approach relies on the use of multilingual emotion embedding that can capture affective information in a language-independent manner. We show that this embedding can be used to predict the pitch and duration of speech units in a target language, allowing us to resynthesize the source speech signal with the same emotional content. We evaluate our approach to English and French speech signals and show that it outperforms a baseline method that does not use emotional information, including when the emotion embedding is extracted from a different language. Even if this preliminary study does not address directly the machine translation issue, our results demonstrate the effectiveness of our approach for cross-lingual emotion preservation in the context of speech resynthesis.

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BibTeX RIS

Jarod Duret, Titouan Parcollet, Yannick Estève. 2023-06-29. Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data. https://arxiv.org/abs/2306.17199

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