Posterior features for template-based ASR

This paper investigates the use of phoneme class conditional probabilities as features (posterior features) for template-based ASR. Using 75 words and 600 words task-independent and speaker-independent setup on Phonebook database, we investigate the use of different posterior distribution estimators, different distance measures that are better suited for posterior distributions, and different training data. The reported experiments clearly demonstrate that posterior features are always superior, and generalize better than other classical acoustic features (at the cost of training a posterior distribution estimator).