Minimum error training for speech recognition

In recent years several research groups have investigated the use of a new framework for minimizing the error rate of a classifier. The key idea is to define a smooth, differentiable loss function that incorporates all adaptable classifier parameters and that approximates the (non-smooth) actual performance error rate. This framework is applicable to a variety of classifier structures, including feedforward neural networks, learning vector quantization classifiers, and hidden Markov models. Here we describe a particular application in which a relatively simple distance-based classifier is trained to minimize errors in speech recognition tasks. The loss function is defined so as to reflect errors at the level of the final, grammar-driven recognition output. We show how the loss function can be made to reflect not just correctness/incorrectness at the string level, but also, for instance, a word spotting loss between the recognized string and the correct string. Thus, minimization of this loss can explicitly optimize the word spotting rate