Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data

• URL: http://arxiv.org/abs/2306.17199v1
• Date: Thu, 29 Jun 2023 08:06:54 GMT
• Title: Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data
• Authors: Jarod Duret (LIA), Titouan Parcollet (CAM), Yannick Est\`eve (LIA)
• Abstract summary: We propose a method for speech-to-speech emotion translation that operates at the level of discrete speech units. We show that this embedding can be used to predict the pitch and duration of speech units in a target language. We evaluate our approach to English and French speech signals and show that it outperforms a baseline method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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