@inproceedings{lujan-etal-2008-evaluation,
title = "Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation",
author = "Luj{\'a}n, M{\'\i}riam and
Mart{\'\i}nez, Carlos D. and
Alabau, Vicent",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Tapias, Daniel",
booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
month = may,
year = "2008",
address = "Marrakech, Morocco",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/217_paper.pdf",
abstract = "Multilingual Automatic Speech Recognition (ASR) systems are of great interest in multilingual environments. We studied the case of the Comunitat Valenciana where the two official languages are Spanish and Valencian. These two languages share most of their phonemes, and their syntax and vocabulary are also quite similar since they have influenced each other for many years. We constructed a system, and trained its acoustic models with a small corpus of Spanish and Valencian, which has produced poor results due to the lack of data. Adaptation techniques can be used to adapt acoustic models that are trained with a large corpus of a language inr order to obtain acoustic models for a phonetically similar language. This process is known as language adaptation. The Maximum Likelihood Linear Regression (MLLR) technique has commonly been used in speaker adaptation; however we have used MLLR in language adaptation. We compared several MLLR variants (mean square, diagonal matrix and full matrix) for language adaptation in order to choose the best alternative for our system.",
}
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<abstract>Multilingual Automatic Speech Recognition (ASR) systems are of great interest in multilingual environments. We studied the case of the Comunitat Valenciana where the two official languages are Spanish and Valencian. These two languages share most of their phonemes, and their syntax and vocabulary are also quite similar since they have influenced each other for many years. We constructed a system, and trained its acoustic models with a small corpus of Spanish and Valencian, which has produced poor results due to the lack of data. Adaptation techniques can be used to adapt acoustic models that are trained with a large corpus of a language inr order to obtain acoustic models for a phonetically similar language. This process is known as language adaptation. The Maximum Likelihood Linear Regression (MLLR) technique has commonly been used in speaker adaptation; however we have used MLLR in language adaptation. We compared several MLLR variants (mean square, diagonal matrix and full matrix) for language adaptation in order to choose the best alternative for our system.</abstract>
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%0 Conference Proceedings
%T Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation
%A Luján, Míriam
%A Martínez, Carlos D.
%A Alabau, Vicent
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Tapias, Daniel
%S Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)
%D 2008
%8 May
%I European Language Resources Association (ELRA)
%C Marrakech, Morocco
%F lujan-etal-2008-evaluation
%X Multilingual Automatic Speech Recognition (ASR) systems are of great interest in multilingual environments. We studied the case of the Comunitat Valenciana where the two official languages are Spanish and Valencian. These two languages share most of their phonemes, and their syntax and vocabulary are also quite similar since they have influenced each other for many years. We constructed a system, and trained its acoustic models with a small corpus of Spanish and Valencian, which has produced poor results due to the lack of data. Adaptation techniques can be used to adapt acoustic models that are trained with a large corpus of a language inr order to obtain acoustic models for a phonetically similar language. This process is known as language adaptation. The Maximum Likelihood Linear Regression (MLLR) technique has commonly been used in speaker adaptation; however we have used MLLR in language adaptation. We compared several MLLR variants (mean square, diagonal matrix and full matrix) for language adaptation in order to choose the best alternative for our system.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/217_paper.pdf
Markdown (Informal)
[Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation](http://www.lrec-conf.org/proceedings/lrec2008/pdf/217_paper.pdf) (Luján et al., LREC 2008)
ACL