Speech recognition in noisy environments: a survey
Speech Communication
Discriminative analysis for feature reduction in automatic speech recognition
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
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This paper examines the performance of feature extraction techniques and the classification of speech represented by continuous Marathi speech model. The goal of this study is to present independent as well as comparative performances of most popular appearance based feature extraction techniques i.e. Principal Component Analysis, Linear Discriminative Analysis, Mel Frequency Cepstrum Coefficient and Discrete Wavelet Transformation. To improve performance of speech recognition, it needed to perform dimensionality reduction of speech data. The Motivation was the lack of direct and detailed independent comparison in all possible algorithms. Mel Frequency Cepstrum Coefficient (MFCC) help us in extracting feature where as Principal Component Analysis (PCA), linear discriminate analysis (LDA), and Discrete Wavelet Transformation are used for reducing dimension of extracted feature. We experimented with several combinations by combining techniques such as MFCC+PCA (MFPCA), MFCC+LDA (MFLDA), MFCC+DWT (MFDWT), MFCC+PCA+DWT (MFPDWT) and MFCC+LDA+ DWT (MFLDWT).The performance of technique is compared on accuracy as well as real time factor. The MFCC+LDA+DWT (MFLDWT) is a best combination which provides the accuracy of 95.06% where real time factor is 0.35 second as compared to other combination.