Segmentation and Disfluency Removal for Conversational Speech Translation

In this paper we focus on the effect of on-line speech segmentation and disfluency removal methods on conversational speech translation. In a real-time conversational speech to speech translation system, on-line segmentation of speech is required to avoid latency beyond few seconds. While sentential unit segmentation and disfluency removal have been heavily studied mainly for off-line speech processing, to the best of our knowledge, the combined effect of these tasks on conversational speech translation has not been investigated. Furthermore, optimization of performance given maximum allowable system latency to enable a conversation is a newer problem for these tasks. We show that the conventional assumption of doing segmentation followed by disfluency removal is not the best practice. We propose a new approach to do simple-disfluency removal followed by segmentation and then by complex-disfluency removal. The proposed approach shows a significant gain on translation performance of up to 3 Bleu points with only 6 second latency to look ahead, using state-of the art machine translation and speech recognition systems.