Investigating translation of Parliament speeches

Abstract

This paper reports on recent experiments for speech to text (STT) translation of European Parliamentary speeches. A Spanish speech to English text translation system has been built using data from the TC-STAR European project. The speech recognizer is a state-of-the-art multipass system trained for the Spanish EPPS task and the statistical translation system relies on the IBM-4 model. First, MT results are compared using manual transcriptions and 1-best ASR hypotheses with different word error rates. Then, an n-best interface between the ASR and MT components is investigated to improve the STT process. Derivation of the fundamental equation for machine translation suggests that the source language model is not necessary for STT. This was investigated by using weak source language models and by n-best rescoring adding the acoustic model score only. A significant loss in the BLEU score was observed suggesting that the source language model is needed given the insufficiencies of the translation model. Adding the source language model score in the n-best rescoring process recovers the loss and slightly improves the BLEU score over the 1-best ASR hypothesis. The system achieves a BLEU score of 37.3 with an ASR word error rate of 10% and a BLEU score of 40.5 using the manual transcripts.