Hassan Sawaf


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From Speech-to-Speech Translation to Automatic Dubbing
Marcello Federico | Robert Enyedi | Roberto Barra-Chicote | Ritwik Giri | Umut Isik | Arvindh Krishnaswamy | Hassan Sawaf
Proceedings of the 17th International Conference on Spoken Language Translation

We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing. Our architecture features neural machine translation generating output of preferred length, prosodic alignment of the translation with the original speech segments, neural text-to-speech with fine tuning of the duration of each utterance, and, finally, audio rendering to enriches text-to-speech output with background noise and reverberation extracted from the original audio. We report and discuss results of a first subjective evaluation of automatic dubbing of excerpts of TED Talks from English into Italian, which measures the perceived naturalness of automatic dubbing and the relative importance of each proposed enhancement.


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Correcting Keyboard Layout Errors and Homoglyphs in Queries
Derek Barnes | Mahesh Joshi | Hassan Sawaf
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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Selective Combination of Pivot and Direct Statistical Machine Translation Models
Ahmed El Kholy | Nizar Habash | Gregor Leusch | Evgeny Matusov | Hassan Sawaf
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Language Independent Connectivity Strength Features for Phrase Pivot Statistical Machine Translation
Ahmed El Kholy | Nizar Habash | Gregor Leusch | Evgeny Matusov | Hassan Sawaf
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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On the Use of Grammar Based Language Models for Statistical Machine Translation
Hassan Sawaf | Kai Schütz | Hermann Ney
Proceedings of the Sixth International Workshop on Parsing Technologies

In this paper, we describe some concepts of language models beyond the usually used standard trigram and use such language models for statistical machine translation. In statistical machine translation the language model is the a-priori knowledge source of the system about the target language. One important requirement for the language model is the correct word order, given a certain choice of words, and to score the translations generated by the translation model \Pr(f1J/eI1), in view of the syntactic context. In addition to standard m-grams with long histories, we examine the use of Part-of-Speech based models as well as linguistically motivated grammars with stochastic parsing as a special type of language model. Translation results are given on the VERBMOBIL task, where translation is performed from German to English, with vocabulary sizes of 6500 and 4000 words, respectively.