Chetouani, M., Gas, B. and Zarader, J. (2002) Discriminative Training for Neural Predictive Coding Applied to Speech Features Extraction. Proceedings of the 2002 International Joint Conference on Neural Networks, 1, 852-857.
http://dx.doi.org/10.1109/ijcnn.2002.1005585

has been cited by the following article:

• TITLE: Robust Speech Recognition System Using Conventional and Hybrid Features of MFCC, LPCC, PLP, RASTA-PLP and Hidden Markov Model Classifier in Noisy Conditions

  AUTHORS: Veton Z. Këpuska, Hussien A. Elharati

  KEYWORDS: Speech Recognition, Noisy Conditions, Feature Extraction, Mel-Frequency Cepstral Coefficients, Linear Predictive Coding Coefficients, Perceptual Linear Production, RASTA-PLP, Isolated Speech, Hidden Markov Model

  JOURNAL NAME: Journal of Computer and Communications, Vol.3 No.6, May 26, 2015

  ABSTRACT: In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.