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        <title>Hierarchical Phrase-Based MT for Phonetic Representation-Based Speech Translation</title>
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    <abstract>The paper presents a novel technique for speech translation using hierarchical phrased-based 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-to-phoneme 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.</abstract>
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%0 Conference Proceedings
%T Hierarchical Phrase-Based MT for Phonetic Representation-Based Speech Translation
%A Ahmed, Zeeshan
%A Jiang, Jie
%A Carson-Berndsen, Julie
%A Cahill, Peter
%A Way, Andy
%S Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2012
%8 oct 28 nov 1
%I Association for Machine Translation in the Americas
%C San Diego, California, USA
%F ahmed-etal-2012-hierarchical
%X The paper presents a novel technique for speech translation using hierarchical phrased-based 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-to-phoneme 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.
%U https://aclanthology.org/2012.amta-papers.1

Markdown (Informal)

[Hierarchical Phrase-Based MT for Phonetic Representation-Based Speech Translation](https://aclanthology.org/2012.amta-papers.1) (Ahmed et al., AMTA 2012)

• Hierarchical Phrase-Based MT for Phonetic Representation-Based Speech Translation (Ahmed et al., AMTA 2012)

ACL

• Zeeshan Ahmed, Jie Jiang, Julie Carson-Berndsen, Peter Cahill, and Andy Way. 2012. Hierarchical Phrase-Based MT for Phonetic Representation-Based Speech Translation. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA. Association for Machine Translation in the Americas.