End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation
This addresses the challenge of real-life conversational speech translation for applications like meeting transcription, though it is incremental as it builds on existing methods.
The paper tackles the problem of single-channel multi-speaker conversational speech translation, which conventional systems struggle with, by proposing an end-to-end multi-task model that outperforms reference systems in multi-speaker conditions while maintaining comparable performance in single-speaker conditions.
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.