Improved model adaptation approach for recognition of reduced-frame-rate continuous speech.

In distributed speech recognition applications, the front-end device that stands for any handheld electronic device like smartphones and personal digital assistants (PDAs) captures the speech signal, extracts the speech features, and then sends the speech-feature vector sequence to the back-end server for decoding. Since the front-end mobile device has limited computation capacity, battery power and bandwidth, there exists a feasible strategy of reducing the frame rate of the speech-feature vector sequence to alleviate the drawback. Previously, we proposed a method for adjusting the transition probabilities of the hidden Markov model to enable it to address the degradation of recognition accuracy caused by the frame-rate mismatch between the input and the original model. The previous model adaptation method is referred to as the adapting-then-connecting approach that adapts each model individually and then connects the adapted models to form a word network for speech recognition. We have found that this model adaption approach introduces transitions that skip too many states and increase the number of insertion errors. In this study, we propose an improved model adaptation approach denoted as the connecting-then-adapting approach that first connects the individual models to form a word network and then adapts the connected network for speech recognition. This new approach calculates the transition matrix of a connected model, adapts the transition matrix of the connected model according to the frame rate, and then creates a transition arc for each transition probability. The new approach can better align the speech feature sequence with the states in the word network and therefore reduce the number of insertion errors. We conducted experiments to investigate the effectiveness of our new approach and analyzed the results with respect to insertion, deletion, and substitution errors. The experimental results indicate that the proposed new method obtains a better recognition rate than the old method.