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Language sample analysis is accepted as the gold standard in child language assessment. Unfortunately it is often viewed as too time consuming for the practicing clinician. Over the last 15 years a great deal of research has been invested in the automated analysis of child language samples to make the process more time efficient. One step in the analysis process may be precoding the sample, as is used in the Systematic Analysis of Language Transcripts (SALT) software. However, a claim has been made (MacWhinney, 2008) that such precoding in fact leads to lower accuracy because of manual coding errors. No data on this issue have been published. The current research measured the accuracy of language samples analyzed with and without SALT precoding. This study also compared the accuracy of current software to an older version called GramCats (Channell & Johnson 1999). The results presented support the use of precoding schemes such as SALT and suggest that the accuracy of automated analysis has improved over time.

Thesis (M.Sc.)--York University, 2007. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 122-124). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR31995

Thesis (M.App.Sc.) - Carleton University, 2003. / Includes bibliographical references (p. 113-119). Also available in electronic format on the Internet.

Thesis (doctoral)--Helsinki University of Technology, 2002. / Includes bibliographical references (p. 123-129). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Oregon, 2004. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 152-155). Also available for download via the World Wide Web; free to University of Oregon users.

A renewed focus on foreign language (FL) learning and speech for communication has resulted in computer-assisted language learning (CALL) software developed with Automatic Speech Recognition (ASR). ASR features for FL pronunciation (Lafford, 2004) are functional components of CALL designs used for FL teaching and learning. The ASR features available with the TeLL me More French software provides pronunciation, intonation and speaking practice and feedback. ASR features are examined quantitatively through French student performance of recorded ASR-scored speech and compared with human raters of the same produced speech samples. A comparison of ASR scores to human raters considers the validity of ASR-scored feedback for individualized and FL classroom instruction. Qualitative analyses of student performances and perceptions of ASR are evaluated using an online survey linked to individual pronunciations and performance and examined for positive impact (Chapelle, 2001) and usability.

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