Summary of the paper

Title Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation.
Authors Míriam Luján Mares, Carlos David Martínez Hinarejos and Vicent Alabau Gonzalvo
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.
Language Multiple languages
Topics Multilinguality, Speech recognition and understanding, Tools, systems, applications
Full paper Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation.
Slides -
Bibtex @InProceedings{LUJNMARES08.217,
  author = {Míriam Luján Mares, Carlos David Martínez Hinarejos and Vicent Alabau Gonzalvo},
  title = {Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation.},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  month = {may},
  date = {28-30},
  address = {Marrakech, Morocco},
  editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-4-0},
  note = {http://www.lrec-conf.org/proceedings/lrec2008/},
  language = {english}
  }

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