Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning

• URL: http://arxiv.org/abs/2008.09483v1
• Date: Thu, 20 Aug 2020 09:37:28 GMT
• Title: Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning
• Authors: No\'e Tits, Kevin El Haddad, Thierry Dutoit
• Abstract summary: 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.
• Score: 6.514358246805895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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