Abstract
This paper presents a novel approach to the identification of phonetic similarity using properties observed during the speech recognition process. Experiments are presented whereby specific phones are removed during the training phase of a statistical speech recognition system so that the behaviour of the system can be analysed to see which alternative phone is selected. The domain of the analysis is restricted to specific contexts and the alternatively recognised (or substituted) phones are analysed with respect to a number of factors namely, the common phonetic properties, the phonetic neighbourhood and the frequency of occurrence with respect to a particular corpus. The results indicate that a measure of phonetic similarity based on alternatively recognised observed properties can be predicted based on a combination of these factors and as such can serve as an important additional source of information for the purposes of modelling pronunciation variation.
This research is supported by the Science Foundation Ireland (Grant 07/CE/I1142) as part of the Centre for Next Generation Localisation ( www.cngl.ie ) at University College Dublin and Dublin City University. The opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of Science Foundation Ireland.
The research presented in this paper is a revised and extended version of the paper presented at LTC 2009, Poznań Poland.
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Kane, M., Mauclair, J., Carson-Berndsen, J. (2011). Automatic Identification of Phonetic Similarity Based on Underspecification. In: Vetulani, Z. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2009. Lecture Notes in Computer Science(), vol 6562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20095-3_5
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DOI: https://doi.org/10.1007/978-3-642-20095-3_5
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