Context Dependent Modeling of Phones in Continuous Speech Using Decision Trees

Context Dependent Modeling of Phones in Continuous Speech Using Decision Trees L.R. Bahl, P.V. de Souza, P.S. Gopalakrishnan,D. Nahamoo, M.A. Picheny IBM Research Division Thomas J. Watson Research Center P.O. Box 704, Yorktown Heights, NY 10598 ABSTRACT In a continuous speech recognition system it is impor- tant to model the context dependent variations in the pro- nunciations of words. In this paper we present an automatic method for modeling phonological variation using decision trees. For each phone we construct a decision tree that spec- ifies the acoustic realization of the phone as a function of the context in which it appears. Several thousand sentences from a natural language corpus spoken by several talkers are used to construct these decision trees. Experimental results on a 5000-word vocabulary natural language speech recog- nition task are presented. INTRODUCTION It is well known that the pronunciation of a word or subword unit such as a phone depends heavily on the context. This phenomenon has been studied ex- tensively by phoneticians who have constructed sets of phonological rules that explain this context dependence [8, 14]. t[owever, the use of such rules in recognition systems has not been extremely successful. Perhaps, a fundamental problem with this approach is that it re- lies on human perception rather than acoustic reality. Furthermore, this method only identifies gross changes, and the more subtle changes, which are generally unim- portant to humans but may be of significant value in speech recognition by computers, are ignored. Possibly, rules constructed with the aid of spectrograms would be more useflfl, but this would be very tedious and dif- ficult. In this paper we describe an automatic method for modeling the context dependence of pronunciation. ]n particular, we expand on the use of decision trees for modeling allophonic variation, which we previously out- lined in [4]. Other researchers have modeled a.ll distinct sequences o[ tri-phones (three consecutive phones) in an effort to capture phonological variations [16, 10]. The method proposed in this paper has the advantage that it allows us to account for much longer contexts. In the experiments reported in this paper, we model the pron0nciation of a phone as a function of the five preceding and five [ollowing phones. This method also has better powers o[ generalization, i.e. modeling con- texts tha.t do not occur in the training data,. Use of decision trees for identifying allophones have been considered in [4, 7, 1], ]5]. However, apart from [4], these methods have either not been used in a rec- ognizer or have not provided significant improvements over existing modeling methods. In the next section we describe the algorithnls used for constructing the decision trees. In Section 3 we present recognition results for a 5000-word natural lan- guage continuous speech recognition task. We also present results showing the the effect of varying tree size and context on the recognition accuracy. Conclud- ing remarks are presented in Section 4. CONSTRUCTING THE DECISION TREE The data used for constructing tile decision trees is obtained from a …

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