The Bayes decision theory is the foundation of the classical statistical pattern recognition approach, with the expected error as the performance objective. For most pattern recognition problems, the “error” is conventionally assumed to be binary, i.e., 0 or 1, equivalent to error counting, independent of the specifics of the error made by the system. The term “error rate” is thus long considered the prevalent system performance measure. This performance measure, nonetheless, may not be satisfactory in many practical applications. In automatic speech recognition, for example, it is well known that some errors are more detrimental (e.g., more likely to lead to misunderstanding of the spoken sentence) than others. In this paper, we propose an extended framework for the speech recognition problem with non-uniform classification/recognition error cost which can be controlled by the system designer. In particular, we address the issue of system model optimization when the cost of a recognition error is class dependent. We formulate the problem in the framework of the minimum classification error (MCE) method, after appropriate generalization to integrate the class-dependent error cost into one consistent objective function for optimization. We present a variety of training scenarios for automatic speech recognition under this extended framework. Experimental results for continuous speech recognition are provided to demonstrate the effectiveness of the new approach.

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.

The classical Bayes decision theory [3] is the foundation of statistical pattern recognition. In [4], we have addressed the issue of non-uniform error criteria in statistical pattern recognition, and generalized the Bayes decision theory for pattern recognition tasks where errors over different classes have varying degrees of significance. We further introduced the weighted minimum classification error (MCE) method for a practical design of a statistical pattern recognition system to achieve empirical optimality when non-uniform error criteria are prescribed. However, one key issue in the weighted MCE method, the methodology of building a suitable non-uniform error cost function given the userpsilas requirements, has not been addressed yet. In this paper, we propose some viable techniques for the design of the non-uniform error cost function in the context of automatic speech recognition (ASR) according to different training scenarios. The experimental results on the TIDIGITS database [8] are presented to demonstrate the effectiveness of our methodologies.

In this paper, we demonstrate the use of learning with non-uniform error-cost as a novel technique to design a multiclass cost-sensitive classifier. We investigate two important aspects of the design. First, we show that the learning is effective enough for active control of the multiclass confusion matrix using the cost-matrix. Second, we study the cases when the classifiers have mild model mismatch problems, and conclude that our design still have better performance compared to the conventional cost-sensitive classifier design.

The Bayes decision theory is the foundation of the classical statistical pattern recognition approach. For most of pattern recognition problems, the Bayes decision theory is employed assuming that the system performance metric is defined as the simple error counting, which assigns identical cost to each recognition error. However, this prevalent performance metric is not desirable in many practical applications. For example, the cost of "recognition" error is required to be differentiated in keyword spotting systems. In this paper, we propose an extended framework for the speech recognition problem with non-uniform classification/recognition error cost. As the system performance metric, the recognition error is weighted based on the task objective. The Bayes decision theory is employed according to this performance metric and the decision rule with a non-uniform error cost function is derived. We argue that the minimum classification error (MCE) method, after appropriate generalization, is the most suitable training algorithm for the "optimal" classifier design to minimize the weighted error rate. We formulate the weighted MCE (W-MCE) algorithm based on the conventional MCE infrastructure by integrating the error cost and the recognition error count into one objective function. In the context of automatic speech recognition (ASR), we present a variety of training scenarios and weighting strategies under this extended framework. The experimental demonstration for large vocabulary continuous speech recognition is provided to support the effectiveness of our approach.

This paper presents an investigation of the rescoring performance using hidden Markov model (HMM) based attribute detectors. The minimum verification error (MVE) criterion is employed to enhance the reliability of the detectors in continuous speech recognition. The HMM-based detectors are applied on the possible recognition candidates, which are generated from the conventional decoder and organized in phone/word graphs. We focus on the study of rescoring performance with the detectors trained on the tokens produced by the decoder but labeled in broad phonetic categories rather than the phonetic identities. Various training criteria and knowledge fusion methods are investigated under various semantic level rescoring scenarios. This research demonstrates various possibilities of embedding auxiliary information into the current automatic speech recognition (ASR) framework for improved results. It also represents an intermediate step towards the construction of a true detection-based ASR paradigm.