A Connectionist Expert Approach for Speech Recognition

Halima Bahi and Mokhtar Sellami

Department of Computer Science, University of Annaba, Algeria

Abstract: Artificial Neural Networks (ANNs) are widely and successfully used in speech recognition, but still many limitations are inherited to their topologies and learning style. In an attempt to overcome these limitations, we combine in a speech recognition hybrid system the pattern processing of ANNs and the logical inferencing of symbolic approaches. In particular, we are interested in the Connectionist Expert System (CES) introduced by Gallant [10], it consists of an expert system implemented throughout a Multi Layer Perceptron (MLP). In such network, each neuron has a symbolic significance. This will overcome one of the difficulties encountered when we built an MLP, which is how to find the appropriate network configuration and will provide it with explanation capabilities. In this paper, we present a CES dedicated to Arabic speech recognition. So, we implemented a neural network where the input neurons represent the acoustical level, they are defined using the vector quantization techniques. The hidden layer represents the phonetic level and according to the Arabic particularities, the used phonetic unit is the syllable. Finally, the output neurons stand for the lexical level, since they are the vocabulary words.

Keywords: Artificial intelligence, speech recognition, hybrid system, neuro-symbolic integration, expert system, neural networks.

Received February 23, 2004; accepted July 8, 2004