Hierarchical Phrase-Based MT for Phonetic Representation-Based Speech Translation.
Zeeshan Ansar Ahmed, Jie Jiang, Julie Carson-Berndsen, Peter J. Cahill, Andy Way · 2012
The paper presents a novel technique for speech translation using hierarchical phrasedbased statistical machine translation (HPB-SMT). 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 English-Chinese 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.