language: en

[Pages 61 to 70 of W. Daelemans, A. van den Bosch, and A. Weijters (Editors), Workshop Notes of the ECML/MLnet Workshop on Empirical Learning of Natural Language Processing Tasks, April 26, 1997, Prague, Czech Republic]

Automatic Phonetic Transcription of Words Based On Sparse Data

Maria Wolters (i) and Antal van den Bosch (ii)
(i) Institut fur Kommunikationsforschung und Phonetik, Universitat Bonn
Poppelsdorfer Allee 47, 53113 Bonn, Germany
mwo@asl1.ikp.uni-bonn.de
(ii) Department of Computer Science, Universiteit Maastricht
PO Box 616, 6200 MD Maastricht, The Netherlands
antal@cs.unimaas.nl

Abstract The relation between the orthography and the phonology of a language has traditionally been modelled by hand-crafted rule sets. Machine-learning (ML) approaches offer a means to gather this knowledge automatically. Problems arise when the training material is sparse. Generalising from sparse data is a well-known problem for many ML algorithms. We present experiments in which connectionist, instance-based, and decision-tree learning algorithms are applied to a small corpus of Scottish Gaelic. instance-based learning in the ib1-ig algorithm yields the best generalisation performance, and that most algorithms tested perform tolerably well. Given the availability of a lexicon, even if it is sparse, ML is a valuable and efficient tool for automatic phonetic transcription of written text.

1 The Problem