HMM representation of quantized articulatory features for recognition of highly confusable words

A speech recognizer based on a hidden Markov model (HMM) representation of quantized articulatory features is described, and experimental results for its evaluation are presented. Traditional schemes for HMM representation of speech have attempted to model a set of disjoint time segments. In order to create a more robust speech recognition system, the speech production system is characterized by a set of articulatory features, each of which are allowed to vary over a range of discrete values. Target configurations of articulators are represented by sets of feature values. These feature values are permitted to vary independently and asynchronously (with appropriate constraints) as the production system moves from one target configuration to the next. This avoids the abrupt model changes inherent in non-overlapping segment modeling. The feature value combinations that occur while in transit between target configurations represent the coarticulation intervals between the two targets. This scheme is implemented using an ergodic HMM to control the evolution of the feature values as the system moves from one target configuration to the next. Speech recognition results show that the new system consistently outperforms the traditional HMM approaches.<>