Abstract:
This paper explains how some properties of the Self-Organizing Maps (SOMs) can be exploited in the density models used in continuous density hidden Markov models (HMMs). The three main ideas are the suitable initialization of the centroids for the Gaussian mixtures, the smoothing of the HMM parameters and the use of topology for fast density approximations. The methods are tested here in the automatic speech recognition framework, where the task is to decode the phonetic transcription of spoken words by speaker dependent, but vocabulary independent phoneme models. The results show that the average number of final recognition errors will be over 15 \% smaller, if the traditional K-means based initialization is substituted by SOM. The method described for fast SOM density approximation improves the total recognition time by over 40 \% for the current online system compared to the default, which uses independent complete searches for the best matching units.