A ROBUST SPEECH RECOGNITION SYSTEM USING A GENERAL REGRESSION NEURAL NETWORK - Amrouche, Abderrahmane; Taleb-Ahmed, Abdelmalik; Rouvaen, Jean Michel; Yagoub, Mustapha C. E. (International Journal of Computing, 2014)
General Regression Neural Networks (GRNN) have been applied to phoneme identification and isolated word recognition in clean speech. In this paper, the authors extended this approach to Arabic spoken word recognition in adverse conditions. In fact, noise robustness is one of the most challenging problems in Automatic Speech Recognition (ASR) and most of the existing recognition methods, which have shown to be highly efficient under noise free conditions, drastically fail drastically in noisy environments. The proposed system has been tested for Arabic digit recognition at different Signal-to-Noise Ratio (SNR) levels and under four noisy conditions: multitalker babble background, car production hall (factory), military vehicle (leopard tank) and fighter jet cockpit (buccaneer) issued from NOISEX-92 data base. The proposed scheme was successfully compared to similar recognizers based on the Multilayer Perceptron (MLP), the Elman Recurrent Neural Network (RNN) and the discrete Hidden Markov Model (HMM). The experimental results show that the use of nonparametric regression with an appropriate smoothing factor (spread) improved the generalization power of the neural network and the global performance of the speech recognizer in noisy environments.