High-quality bilingual subtitle document alignments with application to spontaneous speech translation

In this paper, we investigate the task of translating spontaneous speech transcriptions by employing aligned movie subtitles in training a statistical machine translator (SMT). In contrast to the lexical-based dynamic time warping (DTW) approaches to bilingual subtitle alignment, we align subtitle documents using time-stamps. We show that subtitle time-stamps in two languages are often approximately linearly related, which can be exploited for extracting high-quality bilingual subtitle pairs. On a small tagged data-set, we achieve a performance improvement of 0.21 F-score points compared to traditional DTW alignment approach and 0.39 F-score points compared to a simple line-fitting approach. In addition, we achieve a performance gain of 4.88 BLEU score points in spontaneous speech translation experiments using the aligned subtitle data obtained by the proposed alignment approach compared to that obtained by the DTW based alignment approach demonstrating the merit of the time-stamps based subtitle alignment scheme.