A minimum error approach to speech and pattern recognition

The authors present a new formulation of the pattern recognition problem, aimed at achieving a minimum error rate classification. The classical discriminant analysis methodology is blended with the classification rule (traditionally expressed in an operational form) in a new functional form and is used as the design objective criterion to be optimized by numerical search algorithms. The new formulation results in a smooth error function which approximates the empirical error rate for the design sample set arbitrarily closely. The authors have applied the minimum error formulation to several recognition tasks and demonstrated the advantages of the proposed method. In a speech recognition experiment involving the English E-set vocabulary, it was demonstrated that the proposed minimum error method achieves the best recognition performance. It is concluded that the proposed learning method and formulation provide a solid analytical ground for the long-standing minimum error classifier design problem