Non-Uniform error criteria for automatic pattern and speech recognition

The classical Bayes decision theory [1] is the foundation of statistical pattern recognition. Conventional applications of the Bayes decision theory result in ubiquitous use of the maximum a posteriori probability (MAP) decision policy and the paradigm of distribution estimation as practice in the design of a statistical pattern recognition system. In this paper, we address the issue of non-uniform error criteria in statistical pattern recognition, and generalize the Bayes decision theory for pattern recognition tasks where errors over different classes have different degrees of significance. We further propose extensions of the method of minimum classification error (MCE) [2] for a practical design of a statistical pattern recognition system to achieve empirical optimality when non-uniform error criteria are prescribed. In addition, we apply our method upon speech recognition tasks. In the context of automatic speech recognition (ASR), we present a variety of training scenarios and weighting strategies under our framework. The experimental demonstrations for both general pattern recognition and continuous speech recognition are provided to support the effectiveness of our new approach.