Applications of Decision Tree Methodology

This paper describes decision tree methodology and shows how it has been adapted to three diierent problems in speech recognition and understanding at CRIM and at FORWISS. The three problems are: 1. Development of context-dependent phone models (work carried out by CRIM researchers and partly inspired by FORWISS work on polyphones). Here, decision trees determine a characterization of the context of a phone that yields good models for recognition. 2. Deriving rules for semantic interpretation from a semantically annotated corpus. This problem led to the development of \Semantic Classiication Trees" (in Ph.D. work by R. Kuhn under the supervision of R. De Mori Kuh93a]). 3. Recognising prosodic features (work carried out in collaboration between FORWISS and CRIM). From a word sequence, Semantic Classiication Trees (SCTs) can make predictions about such prosodic features as accents and phrase boundaries. In this ongoing research, we plan to create hybrid decision trees that learn rules combining information from the word sequence and acoustic levels, thus increasing the accuracy with which prosodic events are recognised.