Hierarchical Phrase-Based MT for Phonetic Representation-Based Speech Translation

The paper presents a novel technique for speech translation using hierarchical phrasedbased statistical machine translation (HPBSMT). The system is based on translation of speech from phone sequences as opposed to conventional approach of speech translation from word sequences. The technique facilitates speech translation by allowing a machine translation (MT) system to access to phonetic information. This enables the MT system to act as both a word recognition and a translation component. This results in better performance than conventional speech translation approaches by recovering from recognition error with help of a source language model, translation model and target language model. For this purpose, the MT translation models are adopted to work on source language phones using a grapheme-tophoneme component. The source-side phonetic confusions are handled using a confusion network. The result on IWLST'10 EnglishChinese translation task shows a significant improvement in translation quality. In this paper, results for HPB-SMT are compared with previously published results of phrase-based statistical machine translation (PB-SMT) system (Baseline). The HPB-SMT system outperforms PB-SMT in this regard.