An optimization technique for speaker mapping neural networks using minimal classification error criterion

This paper proposes a new optimization technique for speaker mapping neutral network training using the minimal classification error criterion. Recently, neural network modeling has been widely applied to various fields of speech processing. Most neural network applications are classification tasks; however, one of the authors of this paper proposed a speaker mapping neural network as a non-linear continuous mapping application, and showed its effectiveness. On the other hand, the minimal classification error optimization technique has been proposed and applied to several recognition architectures. Since the conventional speaker mapping neural networks have been trained under the minimal distortion criteria, the minimal classification error optimization technique is expected to provide better speaker mapping neural networks. This paper describes the speaker mapping neural network, the minimal classification error optimization technique, derives the algorithm of the minimal classification error optimization technique in the speaker mapping neural network and investigates the relationship between the derived algorithm and the conventional Back Propagation algorithm. Vowel classification experiments are carried out, showing the effectiveness of the proposed algorithm.