Joint Versus Independent Phonological Feature Models within CRF Phone Recognition

We compare the effect of joint modeling of phonological features to independent feature detectors in a Conditional Random Fields framework. Joint modeling of features is achieved by deriving phonological feature posteriors from the posterior probabilities of the phonemes. We find that joint modeling provides superior performance to the independent models on the TIMIT phone recognition task. We explore the effects of varying relationships between phonological features, and suggest that in an ASR system, phonological features should be handled as correlated, rather than independent.