An efficient speech recognition system in adverse conditions using the nonparametric regression

Authors:
Abderrahmane Amrouche;Mohamed Debyeche;Abdelmalik Taleb-Ahmed;Jean Michel Rouvaen;Mustapha C. E. Yagoub
Affiliations:
Faculty of Electronics and Computer Sciences, USTHB, P.O. Box 32, Bab Ezzouar, Algiers 16 111, Algeria;Faculty of Electronics and Computer Sciences, USTHB, P.O. Box 32, Bab Ezzouar, Algiers 16 111, Algeria;LAMIH (UMR CNRS 8530), Valenciennes University, P.O. Box 304, Le Mont Houy 59 313, France;OAE-IEMN (UMR CNRS 8520), Valenciennes University, P.O. Box 304, Le Mont Houy 59 313, France;SITE, University of Ottawa, 800 King Edward, Ottawa, ON, Canada K1N 6N5
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

Citing 6
Cited 6

Review of neural networks for speech recognition

Neural Computation
Connectionist techniques

Survey of the state of the art in human language technology
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Connectionist probability estimators in HMM arabic speech recognition using fuzzy logic

MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Generalized regression neural networks in time-varying environment

IEEE Transactions on Neural Networks
A general regression neural network

IEEE Transactions on Neural Networks

The use of wavelet entropy in conjuction with neural network for Arabic vowels recognition

WSEAS Transactions on Signal Processing
Performances evaluation of GMM-UBM and GMM-SVM for speaker recognition in realistic world

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
SVM based GMM supervector speaker recognition using LP residual signal

ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Arabic vowels recognition based on wavelet average framing linear prediction coding and neural network

Speech Communication
SR-NBS: A fast sparse representation based N-best class selector for robust phoneme classification

Engineering Applications of Artificial Intelligence
Robust regression fusion of GMM-UBM and GMM-SVM normalized scores using G729 bit-stream for speaker recognition over IP

International Journal of Speech Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

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, fail drastically in noisy environments. The proposed system was tested for Arabic digit recognition at different Signal-to-Noise Ratio (SNR) levels and under four noisy conditions: multispeakers babble background, car production hall (factory), military vehicle (leopard tank) and fighter jet cockpit (buccaneer) issued from NOISEX-92 database. The proposed scheme was successfully compared to the similar recognizers based on the Multilayer Perceptrons (MLP), the Elman Recurrent Neural Network (RNN) and the discrete Hidden Markov Model (HMM). The experimental results showed 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.