Algorithm for clustering continuous density HMM by recognition error

This paper presents a clustering algorithm producing multiple whole-word continuous density hidden Markov models (CDHMM) for isolated word recognition systems. The algorithm estimates a minimum number of CDHMM per word that approaches or satisfies a minimum predefined word-dependent recognition accuracy in the training set. Significantly lower memory requirements and a better and more uniformly distributed recognition accuracy among the words of the vocabulary are measured by comparing this algorithm with the modified K-means clustering method.