A supervised approach to the construction of context-sensitive acoustic prototypes

The authors describe a supervised approach to the construction of context-sensitive acoustic prototypes for use in speech recognition systems using allophonic subword hidden Markov models (HMMs). A properly fine partition of the underlying acoustic space(s) is achieved by incorporating contextual supervision to relate the HMM allophonic models to their acoustic manifestations. By decoupling the overall procedure into a clustering phase followed by a pruning phase, it becomes possible to uncover satisfactorily once and for all the general interrelationships between various acoustic subevents, while customizing the acoustic prototypes themselves according to the available training data. This makes for a more efficient utilization of the training sentences, as evidenced by a substantial reduction in the error rate with respect to a baseline system not taking advantage of supervision. The performance of this method is illustrated on an isolated utterance speech recognition task with a vocabulary of 20000 words.<>