String-level MCE for continuous phoneme recognition
Erik McDermott, Shigeru Katagiri · 1997
In this paper, we present results for the Minimum Classi cation Error (MCE) [1] framework for discriminative training applied to tasks in continuous phoneme recognition. The results obtained using MCE are compared with results for Maximum Likelihood Estimation (MLE). We examine the ability of MCE to attain high recognition performance with a small number of parameters. Phoneme-level and string-level MCE loss functions were used as the optimization criteria for a PrototypeBased Minimum Error Classi er (PBMEC) [2] and an HMM [3]. The former was optimized using Generalized Probabilistic Descent, the latter was optimized using an approximated second order method, the Quickprop algorithm. Two databases were used in this evaluation: 1) the ATR 5240 isolated word datasets for 6 speakers, in both speaker-dependent and multi-speaker mode; 2) the TIMIT database. For both databases, MCE training yielded striking gains in performance and classi er compactness compared to MLE baselines. For instance, through MCE training, performance similar to that of the Maximum Likelihood Successive State Splitting algorithm (ML-SSS) [4] could be obtained with 20 times fewer parameters.