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            <title>Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)</title>
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            <namePart type="given">Nicoletta</namePart>
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    <abstract>In this paper we describe an approach that both creates crosslingual acoustic monophone model sets for speech recognition tasks and objectively predicts their performance without target-language speech data or acoustic measurement techniques. This strategy is based on a series of linguistic metrics characterizing the articulatory phonetic and phonological distances of target-language phonemes from source-language phonemes. We term these algorithms the Combined Phonetic and Phonological Crosslingual Distance (CPP-CD) metric and the Combined Phonetic and Phonological Crosslingual Prediction (CPP-CP) metric. The particular motivations for this project are the current unavailability and often prohibitively high production cost of speech databases for many strategically important low- and middle-density languages. First, we describe the CPP-CD approach and compare the performance of CPP-CD-specified models to both native language models and crosslingual models selected by the Bhattacharyya acoustic-model distance metric in automatic speech recognition (ASR) experiments. Results confirm that the CPP-CD approach nearly matches those achieved by the acoustic distance metric. We then test the CPP-CP algorithm on the CPP-CD models by comparing the CPP-CP scores to the recognition phoneme error rates. Based on this comparison, we conclude that the CPP-CP algorithm is a reliable indicator of crosslingual model performance in speech recognition tasks.</abstract>
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%0 Conference Proceedings
%T Borrowing Language Resources for Development of Automatic Speech Recognition for Low- and Middle-Density Languages
%A Melnar, Lynette
%A Liu, Chen
%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 melnar-liu-2008-borrowing
%X In this paper we describe an approach that both creates crosslingual acoustic monophone model sets for speech recognition tasks and objectively predicts their performance without target-language speech data or acoustic measurement techniques. This strategy is based on a series of linguistic metrics characterizing the articulatory phonetic and phonological distances of target-language phonemes from source-language phonemes. We term these algorithms the Combined Phonetic and Phonological Crosslingual Distance (CPP-CD) metric and the Combined Phonetic and Phonological Crosslingual Prediction (CPP-CP) metric. The particular motivations for this project are the current unavailability and often prohibitively high production cost of speech databases for many strategically important low- and middle-density languages. First, we describe the CPP-CD approach and compare the performance of CPP-CD-specified models to both native language models and crosslingual models selected by the Bhattacharyya acoustic-model distance metric in automatic speech recognition (ASR) experiments. Results confirm that the CPP-CD approach nearly matches those achieved by the acoustic distance metric. We then test the CPP-CP algorithm on the CPP-CD models by comparing the CPP-CP scores to the recognition phoneme error rates. Based on this comparison, we conclude that the CPP-CP algorithm is a reliable indicator of crosslingual model performance in speech recognition tasks.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/68_paper.pdf

Markdown (Informal)

[Borrowing Language Resources for Development of Automatic Speech Recognition for Low- and Middle-Density Languages](http://www.lrec-conf.org/proceedings/lrec2008/pdf/68_paper.pdf) (Melnar & Liu, LREC 2008)

• Borrowing Language Resources for Development of Automatic Speech Recognition for Low- and Middle-Density Languages (Melnar & Liu, LREC 2008)

ACL

• Lynette Melnar and Chen Liu. 2008. Borrowing Language Resources for Development of Automatic Speech Recognition for Low- and Middle-Density Languages. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).