Intelligent Rapid Voice Recognition using Neural Tensor Network, SVM and Reinforcement Learning

We propose two machine learning improvements on the existing architecture of voiceand speakerrecognition software. Where conventional systems extract two kinds of frequency data from voice recordings and use the concatenation as input, we propose two methods to allow the input vectors to interact multiplicatively. The first is a Neural Tensor Network layer under a softmax classifier, and the second is a constrained variant of the Neural Tensor Network with reduced dimensionality. We compare these methods with the current approach, using SVMs on the concatenation of extracted data vectors. Second, we trained a shallow neural network on a Q-learning framework in order to intelligently and dynamically minimize the amount of audio required to make an accurate classification decision. While the neural network architectures failed to improve on the existing SVM model, the Q-learner did learn to dynamically minimize audio sampling while improving on the accuracy of the SVM system. Keywords—function approximation, Markov Decision Process, MDP, Mel Frequency Cepstral Coefficients, Neural Network, Neural Tensor Network, MFCC, policy optimization, Q-learning, security, signal processing, Support Vector Machine, SVM, voice authentication, voice recognition.