End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation

• URL: http://arxiv.org/abs/2311.00697v1
• Date: Wed, 1 Nov 2023 17:55:09 GMT
• Title: End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation
• Authors: Juan Zuluaga-Gomez, Zhaocheng Huang, Xing Niu, Rohit Paturi, Sundararajan Srinivasan, Prashant Mathur, Brian Thompson, Marcello Federico
• Abstract summary: We tackle single-channel multi-speaker conversational ST with an end-to-end and multi-task training model. Speaker-Turn Aware Conversational Speech Translation combines automatic speech recognition, speech translation and speaker turn detection. We show that our model outperforms the reference systems on the multi-speaker condition, while attaining comparable performance on the single-speaker condition.
• Score: 23.895122319920997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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