Pattern Recognition Letters
Multi-stream adaptive evidence combination for noise robust ASR
Speech Communication - Special issue on noise robust ASR
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Subband-Based Speech Recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Hidden Neural Networks: A Framework for HMM/NN Hybrids
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
AICCSA 08 The 6th IEEE/ACS international conference on computer systems and applications
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Improvement of the speech recognition in noisy environments using a nonparametric regression
International Journal of Parallel, Emergent and Distributed Systems
An efficient speech recognition system in adverse conditions using the nonparametric regression
Engineering Applications of Artificial Intelligence
The use of wavelet entropy in conjuction with neural network for Arabic vowels recognition
WSEAS Transactions on Signal Processing
Hi-index | 0.00 |
Hidden Markov Models (HMM) are nowadays the most successful modeling approach for speech recognition. However, standard HMM require the assumption that adjacent feature vectors are statistically independent and identically distributed. These assumptions can be relaxed by introducing neural networks in the HMM frame work. These neural networks particularly the Multi-Layer Perceptrons (MLP) estimate the posterior probabilities used by the HMM. We started in the frame work of this work, to investigate smoothing techniques combining MLP probabilities with those from others estimators with better properties for small values (e.g., a single Gaussian) in the framework of the learning of our MLP. The main goal of this paper is to compare the performance of speech recognition of an isolated speech Arabic databases obtained with (1) discrete HMM, (2) hybrid HMM/MLP approaches using a MLP to estimate the HMM emission probabilities and (3) hybrid FCM/HMM/MLP approaches using the Fuzzy C-Means (FCM) algorithm to segment the acoustic vectors.