Pattern classification is an important task in speech recognition and speaker verification. Given the feature vectors of an input the goal is to capture the characteristics of these features unique to each class. This paper deals with exploring Auto Associative Neural Network (AANN) models for the task of speaker verification and speech recognition. We show that AANN models produce comparable performance with that of GMM based speaker verification and speech recognition.
['Sachin Joshi', 'Kishore Prahallad', 'B. Yegnanarayana']
AANN-HMM models for speaker verification and speech recognition