Abstract

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. Non-discriminative techniques compute the likelihood of the observed data assuming an event. Non-discriminative 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

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