Combining Evidence from a Generative and a Discriminative Model in Phoneme Recognition

We investigate the use of the log-likelihood of the features obtained from a generative Gaussian mixture model, and the posterior probability of phonemes from a discriminative multilayered perceptron in multi-stream combination for recognition of phonemes. Multi-stream combination techniques, namely early integration and late integration are used to combine the evidence from these models. By using multi-stream combination, we obtain a phoneme recognition accuracy of 74% on the standard TIMIT database, an absolute improvement of 2.5% over the single best stream.