Summary of the paper

Title First Broadcast News Transcription System for Khmer Language
Authors Sopheap Seng, Sethserey Sam, Laurent Besacier, Brigitte Bigi and Eric Castelli
Abstract In this paper we present an overview on the development of a large vocabulary continuous speech recognition (LVCSR) system for Khmer, the official language of Cambodia, spoken by more than 15 million people. As an under-resourced language, develop a LVCSR system for Khmer is a challenging task. We describe our methodologies for quick language data collection and processing for language modeling and acoustic modeling. For language modeling, we investigate the use of word and sub-word as basic modeling unit in order to see the potential of sub-word units in the case of unsegmented language like Khmer. Grapheme-based acoustic modeling is used to quickly build our Khmer language acoustic model. Furthermore, the approaches and tools used for the development of our system are documented and made publicly available on the web. We hope this will contribute to accelerate the development of LVCSR system for a new language, especially for under-resource languages of developing countries where resources and expertise are limited.
Language Single language
Topics Speech recognition and understanding, Corpus (creation, annotation, etc.), Tools, systems, applications
Full paper First Broadcast News Transcription System for Khmer Language
Slides First Broadcast News Transcription System for Khmer Language
Bibtex @InProceedings{SENG08.661,
  author = {Sopheap Seng, Sethserey Sam, Laurent Besacier, Brigitte Bigi and Eric Castelli},
  title = {First Broadcast News Transcription System for Khmer Language},
  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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