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. We show that our model is able to generate speech, laughter, and other paralinguistic signals in the two channels simultaneously and reproduces more naturalistic and fluid turn taking compared to a text-based cascaded model.1,2.
Original language | English |
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Pages (from-to) | 250-266 |
Number of pages | 17 |
Journal | Transactions of the Association for Computational Linguistics |
Volume | 11 |
DOIs | |
State | Published - 14 Mar 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023 Association for Computational Linguistics.
Funding
In this work, E.D. in his academic role (EHESS, ENS-PSL, CNRS) was supported by the Agence Nationale pour la Recherche (ANR-17-EURE-0017 Frontcog, ANR-10-IDEX-0001-02 PSL*, ANR-19-P3IA-0001 PRAIRIE 3IA Institute), a grant from CIFAR (Learning in Machines and Brains). B.S. was also supported by the Agence Nationale pour la Recherche (ANR-19-P3IA-0001 PRAIRIE 3IA Institute).
Funders | Funder number |
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ANR-10-IDEX-0001-02 | PSL, ANR-19-P3IA-0001 |
ANR-17-EURE-0017 | |
Canadian Institute for Advanced Research | |
Agence Nationale de la Recherche |