Michael D. Riley
ABSTRACT
Several applications of statistical tree-based modelling are described here to problems in speech and language. Classification and regression trees are well suited to many of the pattern recognition problems encountered in this area since they (1) statistically select the most significant features involved (2) provide "honest" estimates of their performance, (3) permit both categorical and continuous features to be considered, and (4) allow human interpretation and exploration of their result. First the method is summarized, then its application to automatic stop classification, segment duration prediction for synthesis, phoneme-to-phone classification, and end-of-sentence detection in text are described. For other applications to speech and language, see [Lucassen 1984], [Bahl, et al 1987].
- Bahl, L., et. al. 1987. A tree-based statistical language model for natural language speech recognition. IBM Research Report 13112.Google Scholar
- Brieman, L., et. al. 1984. Classification and regression trees. Monterey, CA: Wadsworth & Brooks.Google Scholar
- Chou, P. 1988. Applications of information theory to pattern recognition and the desing of decision trees and trellises. Ph.D. thesis, Stanford University, Stanford, CA. Google ScholarDigital Library
- Klatt, D. 1976. Linguistic uses of segmental duration in English: acoustic and perceptual evidence. J. Acoust. Soc. Am. 59. 1208--1221.Google Scholar
- Lucassen, J. M. & Mercer, R. L. 1984. An information theoretic approach to the automatic determination of phonemic baseforms. Proc. ICASSP '84. 42.5.1--42.5.4.Google ScholarCross Ref
- Talkin, D. 1987. Speech formant trajectory estimation using dynamic programming with modulated transition costs. ATT-BL Technical Memo. 11222-87-0715-07.Google Scholar
- Some applications of tree-based modelling to speech and language
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