Title :
AANN-HMM models for speaker verification and speech recognition
Author :
Joshi, S. ; Prahallad, K. ; Yegnanarayana, B.
Author_Institution :
Int. Inst. of Inf. Technol., Hyderabad
Abstract :
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.
Keywords :
neural nets; pattern classification; speaker recognition; auto associative neural network; feature vectors; pattern classification; speaker verification; speech recognition; Feedforward neural networks; Joining processes; Matrix decomposition; Neural networks; Pattern classification; Principal component analysis; Probability; Speaker recognition; Speech recognition; Statistical distributions;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634174
