Maximum margin learning and adaptation of MLP classifiers
Conventional MLP classifiers used in phonetic recognition and speech recognition may encounter local minima during training, and they often lack an intuitive and flexible adaptation approach. This paper presents a hybrid MLP-SVM classifier and its associated adaptation strategy, where the last layer of a conventional MLP is learned and adapted in the maximum separation margin sense. This structure also provides a support vector based adaptation mechanism which better interpolates between a speaker-independent model and speakerdependent adaptation data. Preliminary experiments on vowel classification have shown promising results for both MLP learning and adaptation problems.