Consolidation based speech translation

To alleviate the degradation of the performance of speech translation, this paper proposes a new approach to translate ASR results through consolidation which extracts meaningful phrases and remove redundant and irrelevant information caused by speaker´s disfluency and recognition errors. The speech translation results via consolidation are partial translation and can not be directly compared with gold standards in which all words are translated. We would like to propose a new evaluation framework for partial translation by comparing with the most similar set of words extracted from a word network created by merging gradual summarizations of the gold standard translation. Chinese broadcast news speech in RT04 were recognized, consolidated and then translated. The performance of MT results was evaluated using BLEU. We propose information preservation accuracy (IPAccy) and meaning preservation accuracy (MPAccy) for consolidation and consolidation-based MT.