Generative Spoken Dialogue Language Modeling

• URL: http://arxiv.org/abs/2203.16502v1
• Date: Wed, 30 Mar 2022 17:39:45 GMT
• Title: Generative Spoken Dialogue Language Modeling
• Authors: Tu Anh Nguyen, Eugene Kharitonov, Jade Copet, Yossi Adi, Wei-Ning Hsu, Ali Elkahky, Paden Tomasello, Robin Algayres, Benoit Sagot, Abdelrahman Mohamed, Emmanuel Dupoux
• Abstract summary: We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised spoken unit discovery and a dual-tower transformer architecture. It is able to generate speech, laughter and other paralinguistic signals in the two channels simultaneously and reproduces naturalistic turn taking.
• Score: 46.5879150153352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised spoken unit discovery coupled with a dual-tower transformer architecture with cross-attention trained on 2000 hours of two-channel raw conversational audio (Fisher dataset) without any text or labels. It is able to generate speech, laughter and other paralinguistic signals in the two channels simultaneously and reproduces naturalistic turn taking. Generation samples can be found at: https://speechbot.github.io/dgslm.

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