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Abstract: Automatic language identification (LID) from the spoken speech utterance is a challenging problem. In this paper, we present an LID system that works for South Indian languages and Hindi. Each language is modeled using an approach based on Vector Quantisation [1]. The speech is segmented into di erent sounds (CVs) and the performance of the system on each of the segments is studied. Our studies indicate that the presence of some CVs is crucial for each language. We also find that for the same... (Update)
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BibTeX entry: (Update)
@misc{ balleda-language,
author = "Jyotsana Balleda and Hema A Murthy and T. Nagarajan",
title = "Language Identification from Short Segments of Speech",
url = "citeseer.ist.psu.edu/balleda00language.html" }
Citations (may not include all citations):59 IEEE ASSP Magazine (context) - Gray, Quantization - 1984
53 The OGI multi-language telephone speech corpus - Cole, Muthusamy et al. - 1992
10 Neural Network Models for Recognition of Stop Consonant-Vowe.. (context) - Sekhar - 1996
1 Hidden Markov Models for Speech Recogntion (context) - Huang, Ariki et al.
1 A Spoken Language Identication System for Indian Languages (context) - Balleda - 2000
1 LVCSR - based language identication (context) - Schultz, Rogine et al. - 1996
1 A new model for speaker identication using ane transformatio.. (context) - Balleda, Vela et al. - 1999
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