Enhancing ASR by MT using Semantic Information from HindiWordNet

Aniruddha  Tammewar; Karan  Singla; Srinivas  Bangalore; Michael Carl

Enhancing ASR by MT using Semantic Information from HindiWordNet

Aniruddha Tammewar, Karan Singla, Srinivas Bangalore, Michael Carl

Research output: Contribution to conference › Poster › Research › peer-review

Abstract

In a conventional CAT (Computer Assisted Translation) system a human translator post-edits an automatically generated target language text using the keyboard. In this paper we extend a CAT system with speech input by which the translator speaks the translation, a process refered to as sight translation. We report several experiments to improve the performance of an automatic speech recognition system, taking advantage of machine translation output and information fromWordNet. Overall we outperform a baseline system which has no semantic information by an increased 1.6% word accuracy for the English to Hindi translation.

Original language	English
Publication date	2013
Publication status	Published - 2013
Event	10th International Conference on Natural Language Processing - Centre for Development of Advanced Computing, Noida, India Duration: 18 Dec 2013 → 20 Dec 2013 Conference number: 10 http://ltrc.iiit.ac.in/icon/2013/index.php

Conference

Conference	10th International Conference on Natural Language Processing
Number	10
Location	Centre for Development of Advanced Computing
Country/Territory	India
City	Noida
Period	18/12/2013 → 20/12/2013
Internet address	http://ltrc.iiit.ac.in/icon/2013/index.php

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Cite this

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title = "Enhancing ASR by MT using Semantic Information from HindiWordNet",

abstract = "In a conventional CAT (Computer Assisted Translation) system a human translator post-edits an automatically generated target language text using the keyboard. In this paper we extend a CAT system with speech input by which the translator speaks the translation, a process refered to as sight translation. We report several experiments to improve the performance of an automatic speech recognition system, taking advantage of machine translation output and information fromWordNet. Overall we outperform a baseline system which has no semantic information by an increased 1.6% word accuracy for the English to Hindi translation.",

author = "Aniruddha Tammewar and Karan Singla and Srinivas Bangalore and Michael Carl",

year = "2013",

language = "English",

note = "10th International Conference on Natural Language Processing ; Conference date: 18-12-2013 Through 20-12-2013",

url = "http://ltrc.iiit.ac.in/icon/2013/index.php",

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TY - CONF

T1 - Enhancing ASR by MT using Semantic Information from HindiWordNet

AU - Tammewar, Aniruddha

AU - Singla, Karan

AU - Bangalore, Srinivas

AU - Carl, Michael

N1 - Conference code: 10

PY - 2013

Y1 - 2013

N2 - In a conventional CAT (Computer Assisted Translation) system a human translator post-edits an automatically generated target language text using the keyboard. In this paper we extend a CAT system with speech input by which the translator speaks the translation, a process refered to as sight translation. We report several experiments to improve the performance of an automatic speech recognition system, taking advantage of machine translation output and information fromWordNet. Overall we outperform a baseline system which has no semantic information by an increased 1.6% word accuracy for the English to Hindi translation.

AB - In a conventional CAT (Computer Assisted Translation) system a human translator post-edits an automatically generated target language text using the keyboard. In this paper we extend a CAT system with speech input by which the translator speaks the translation, a process refered to as sight translation. We report several experiments to improve the performance of an automatic speech recognition system, taking advantage of machine translation output and information fromWordNet. Overall we outperform a baseline system which has no semantic information by an increased 1.6% word accuracy for the English to Hindi translation.

M3 - Poster

T2 - 10th International Conference on Natural Language Processing

Y2 - 18 December 2013 through 20 December 2013

ER -