End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation
Juan Pablo Zuluaga-Gomez, Zhaocheng Huang, Xing Niu, Rohit Paturi, Sundararajan Srinivasan, Prashant Mathur, Brian Thompson, Marcello Federico
Abstract
Conventional speech-to-text translation (ST) systems are trained on single-speaker utterances, and they may not generalize to real-life scenarios where the audio contains conversations by multiple speakers. In this paper, we tackle single-channel multi-speaker conversational ST with an end-to-end and multi-task training model, named Speaker-Turn Aware Conversational Speech Translation, that combines automatic speech recognition, speech translation and speaker turn detection using special tokens in a serialized labeling format. We run experiments on the Fisher-CALLHOME corpus, which we adapted by merging the two single-speaker channels into one multi-speaker channel, thus representing the more realistic and challenging scenario with multi-speaker turns and cross-talk. Experimental results across single- and multi-speaker conditions and against conventional ST systems, show that our model outperforms the reference systems on the multi-speaker condition, while attaining comparable performance on the single-speaker condition. We release scripts for data processing and model training.- Anthology ID:
- 2023.emnlp-main.449
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7255–7274
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.449
- DOI:
- 10.18653/v1/2023.emnlp-main.449
- Cite (ACL):
- Juan Pablo Zuluaga-Gomez, Zhaocheng Huang, Xing Niu, Rohit Paturi, Sundararajan Srinivasan, Prashant Mathur, Brian Thompson, and Marcello Federico. 2023. End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7255–7274, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation (Zuluaga-Gomez et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.emnlp-main.449.pdf