Zhaocheng Huang
2023
End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation
Juan Pablo Zuluaga-Gomez
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Zhaocheng Huang
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Xing Niu
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Rohit Paturi
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Sundararajan Srinivasan
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Prashant Mathur
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Brian Thompson
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Marcello Federico
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
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.
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Co-authors
- Juan Pablo Zuluaga-Gomez 1
- Xing Niu 1
- Rohit Paturi 1
- Sundararajan Srinivasan 1
- Prashant Mathur 1
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