Learning from Human Interpreter Speech

Speaker: Matthias Paulik

Title: "Learning from Human Interpreter Speech"
Date: 12 August 2008

Abstract:
Can spoken language translation (SLT) profit from human interpreter speech? In this talk, we explore scenarios which involve live human interpretation, off-line transcription and off-line translation on a massive scale. We consider the deployment of machine translation (MT) and automatic speech recognition (ASR) for the off-line transcription and translation tasks; our systems are trained on 80+ hours of audio data and on parallel text corpora of ~40 million words. To improve performance, we use the available human interpreter speech as an auxiliary information source to bias ASR and MT language models. We evaluate this approach on European Parliament Plenary Session (EPPS) data in three languages (English, Spanish and German), and report preliminary improvements in translation and transcription performance.