A comparison of phoneme decision tree (PDT) and context adaptive phone (CAP) based approaches to vocabulary-independent speech recognition

This paper compares two approaches to context-sensitive phoneme-level hidden Markov modelling for vocabulary-independent automatic speech recognition. The first is an existing method based on the use of binary decision trees to identify equivalence classes of contexts which induce the same effect on the acoustic realisation of a given phoneme. The second is a novel method, called context adaptive phone modelling, which is based on the use of `context-independent generalised phones-in-context´. In the first method equivalence classes of contexts are derived from a direct analysis of the acoustic patterns, whereas the second approach utilises a symbolic transcription of the training corpus. The paper presents an experimental and methodological comparison of the two methods