The number of Web Users accessing the Internet becomes increasing day by day. Any kind of required information can be obtained anytime by anybody from the web. Information retrieval is the fact that there is vast amount of garbage that surrounds any useful information. Such information should be easily accessible and digestible. Internet is no longer monolingual and non-English content is growing rapidly. Speech is easy mode of communication for the people to interact with the computer, rather than using keyboard and mouse. This paper is new attempt to integrate speech recognition and cross language information retrieval system. Speech based queries retrieval performance is compatible with retrieval performance of text based queries. Both Speech recognition and Cross language information retrieval fields are very much challenging to integrate. On Forum for Information Retrieval Evaluation (FIRE) 2011 dataset speech and text query based Tamil- English Cross Language Information Retrieval (CLIR) system achieves 65% and 63% of monolingual retrieval. In future speech based CLIR system very much useful for visually challenged people.
[1]
Eneko Agirre,et al.
Advances in Multilingual and Multimodal Information Retrieval.
,
2008
.
[2]
Sivaji Bandyopadhyay,et al.
Bengali, Hindi and Telugu to English Ad-hoc Bilingual Task at CLEF 2007
,
2007,
CLEF.
[3]
M. Anand Kumar,et al.
A sequence labeling approach to morphological analyzer for Tamil language
,
2010
.
[4]
D. Thenmozhi,et al.
Tamil-English Cross Lingual Information Retrieval System for Agriculture Society
,
2009
.
[5]
Pankaj Kumar.
Statistical Machine Translation Based Punjabi to English Transliteration System for Proper Nouns
,
2013
.
[6]
M. Anusiya,et al.
BILINGUAL TRANSLATION SYSTEM
,
2011
.
[7]
M. F. Porter,et al.
An algorithm for suffix stripping
,
1997
.