Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning

Noé Tits; Kevin El Haddad; Thierry Dutoit

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arXiv · 2008.09483

Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning

Noé Tits·Kevin El Haddad·Thierry Dutoit

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Abstract

Despite the growing interest for expressive speech synthesis, synthesis of nonverbal expressions is an under-explored area. In this paper we propose an audio laughter synthesis system based on a sequence-to-sequence TTS synthesis system. We leverage transfer learning by training a deep learning model to learn to generate both speech and laughs from annotations. We evaluate our model with a listening test, comparing its performance to an HMM-based laughter synthesis one and assess that it reaches higher perceived naturalness. Our solution is a first step towards a TTS system that would be able to synthesize speech with a control on amusement level with laughter integration.

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BibTeX RIS

Noé Tits, Kevin El Haddad, Thierry Dutoit. 2020-08-20. Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning. https://arxiv.org/abs/2008.09483

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