PROSODY AS A CONDITIONING VARIABLE IN SPEECH RECOGNITION
In this paper, we demonstrate two different methods for improving the accuracy and correctness of the standard HMM speech recognizer. The first method involves incorporating prosody into the acoustic model. The second method modifies the HMM so that it can make use of explicit duration probability densities to improve recognition rates. We prove that both of these methods are effective by running two different experiments using the Hidden Markov Toolkit (HTK). The first experiment determines the correctness and accuracy of a model by performing word recognition. The second experiment determines how well a model picks up on the prosody.