PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems

Kentaro Mitsui, Koh Mitsuda, Toshiaki Wakatsuki, Yukiya Hono, Kei Sawada


Abstract
Multimodal language models that process both text and speech have a potential for applications in spoken dialogue systems. However, current models face two major challenges in response generation latency: (1) generating a spoken response requires the prior generation of a written response, and (2) speech sequences are significantly longer than text sequences. This study addresses these issues by extending the input and output sequences of the language model to support the parallel generation of text and speech. Our experiments on spoken question answering tasks demonstrate that our approach improves latency while maintaining the quality of response content. Additionally, we show that latency can be further reduced by generating speech in multiple sequences. Demo samples are available at https://rinnakk.github.io/research/publications/PSLM.
Anthology ID:
2024.findings-emnlp.151
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2692–2700
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.151
DOI:
10.18653/v1/2024.findings-emnlp.151
Bibkey:
Cite (ACL):
Kentaro Mitsui, Koh Mitsuda, Toshiaki Wakatsuki, Yukiya Hono, and Kei Sawada. 2024. PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 2692–2700, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems (Mitsui et al., Findings 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.findings-emnlp.151.pdf