Speech Recognition Using Neural Networks

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SPEECH RECOGNITION USING NEURAL NETWORKS ABSTRACT

Speech processing has been an active area for several decades with a wide variety of applications ranging from communications to automatic reading machines. The development of Speech Recognition Products is mainly based on statistical techniques which work under very specific assumptions. The work presented in this paper investigates the feasibility of an alternative approach for solving the problem more efficiently using Artificial Intelligence (Neural Networks). A speech recognizer system comprises of two distinct blocks, a Feature Extractor and a Recognizer. The Feature Extractor block uses a standard LPC Cepstrum coder, which translates the incoming speech into a trajectory in the LPC Cepstrum feature space, followed by a Self Organizing Map, which tailors the outcome of the coder in order to produce optimal trajectory representations of words in reduced dimension feature spaces. Designs of the Recognizer blocks based on two different approaches are compared. The performance of Multi-Layer Perceptrons, and Recurrent Neural Networks based recognizers is tested. We took the samples of the ten digits (0,1,2…) by the same speaker and examined the accuracy rates based on two different approaches mentioned above. Experimental results indicated that trajectories on such reduced dimension spaces can provided reliable representations of spoken word, while reducing the training complexity and the operation of the Recognizer.