Vector quantization (VQ) has been explored in the past as a means of reducing likelihood computation in speech recognizers which use hidden Markov models (HMMs) containing Gaussian output densities. Although this approach has proved successful, there is an extent beyond which further reduction in likelihood computation substantially degrades the recognition accuracy. Since the components of the VQ frontend are typically designed after model training is complete, this degradation can be attributed to the fact that VQ and HMM parameters are not jointly estimated. In order to restore the accuracy of a recognizer using VQ to aggressively reduce computation, joint estimation is necessary. We propose a technique which couples VQ frontend design with minimum classification error training. We demonstrate on a large vocabulary subword task that in certain cases, our joint training algorithm can reduce the string error rate by 79% compared to that of VQ mixture selection alone.
['Shawn M. Herman', 'Rafid A. Sukkar']
Joint MCE estimation of VQ and HMM parameters for Gaussian mixture selection