Using the self-organizing map to speed up the probability density estimation for speech recognition with mixture density HMMs

This paper presents methods to improve the probability density estimation in hidden Markov models for phoneme recognition by exploiting the self-organizing map (SOM) algorithm. The advantage of using the SOM is based on the created approximative topology between the mixture densities by training the Gaussian mean vectors used as the kernel centers by the SOM algorithm. The topology allows the neighboring mixtures to respond strongly to the same inputs and so most of the nearest mixtures used to approximate the current observation probability will be found in the topological neighborhood of the “winner” mixture. Also the knowledge about the previous winners are used to speed up the search for the new winners. Tree-search SOMs and segmental SOM training are studied aiming at faster search and suitability for HMM training. The framework for the presented experiments includes mel-cepstrum features and phoneme-wise tied mixture density HMMs