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SOM based density function approximation for mixture density HMMs
 In Workshop on SelfOrganizing Maps
, 1997
"... This paper explains how some properties of the SelfOrganizing 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 paramete ..."
Abstract

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This paper explains how some properties of the SelfOrganizing 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 Kmeans 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. 1 About the application The auto...
A Survey of Discriminative and Connectionist Methods for Speech Processing
, 2002
"... Discriminative speech processing techniques attempt to compute the maximum a posterior probability of some speech event, such as a particular phoneme being spoken, given the observed data. Nondiscriminative techniques compute the likelihood of the observed data assuming an event. Nondiscriminative ..."
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Discriminative speech processing techniques attempt to compute the maximum a posterior probability of some speech event, such as a particular phoneme being spoken, given the observed data. Nondiscriminative techniques compute the likelihood of the observed data assuming an event. Nondiscriminative methods such as simple HMMs (hidden Markov models) achieved success despite their lack of discriminative modelling. This survey will look at enhancements to the HMM model which have improved their discrimination ability and hence their overall performance. This survey also reviews alternative discriminative methods, namely connectionist methods such as ANNs (arti cial neural networks). We will also draw comparisons between discriminative HMMs and connectionist models, showing that connectionist models can be viewed as a generalisation of discriminative HMMs. 1