A procedure to generate training sequences for a connected word recognizer using the segmental k-means training algorithm

Past research has shown that a connected digit recognition system, based on either word templates or word hidden Markov models (HMM), could effectively be trained using a segmental k-means training procedure. In these studies, a set of randomly generated digit strings of variable length was used to train the recognizer. However, problems were encountered when this training procedure was extended to systems with medium to large vocabularies. For a training set to be effective, it should represent each vocabulary word and its acoustic variability within the context of all valid input strings defined by a task dependent grammar. A procedure to generate a training set of sentences with these properties is proposed. Using this procedure, a training set of sentences was generated for a connected word recognition system simulating an airline flight reservation task. Several speaker dependent automatic speech recognition (ASR) experiments were performed to assess the effectiveness of the training set generated using the new procedure. The results of these experiments showed that the string accuracy was about 98% when tested on independent sets of test sentences for the three talkers