A Bayesian predictive classification approach to robust speech recognition

We introduce a new Bayesian predictive classi cation (BPC) approach to robust speech recognition and apply the BPC framework to Gaussian mixture continuous density hidden Markov model based speech recognition. We propose and focus on one of the approximate BPC approach called quasiBayesian predictive classi cation (QBPC). In comparison with the standard plug-in maximum a posteriori decoding, when the QBPC method is applied to speaker independent recognition of a confusable vocabulary, namely 26 English letters, where a broad range of mismatches between training and testing conditions exist, the QBPC achieves around 14% relative recognition error rate reduction. While the QBPC method is applied to cross-gender testing on a less confusable vocabulary, namely 20 English digits and commands, the QBPC method achieves around 24% relative recognition error rate reduction.