Autocorrelation-based Methods for Noise-Robust Speech Recognition

Farahani, Gholamreza; Ahadi, Mohammad; Homayounpour, Mohammad Mehdi

doi:10.5772/4753

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…New set of (perceptual linear predictive) PLP vector is autocorrelation domain proposed in Rahali et al [31] and tested in various noisy conditions, and significant improvement is obtained in comparison to traditional feature extraction techniques.…”

Section: Introductionmentioning

confidence: 99%

Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition

Farahani

2017

J AUDIO SPEECH MUSIC PROC.

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Autocorrelation domain is a proper domain for clean speech signal and noise separation. In this paper, a method is proposed to decrease effects of noise on the clean speech signal, autocorrelation-based noise subtraction (ANS). Then to deal with the error introduced by assumption that noise and clean speech signal are uncorrelated, two methods are proposed. Also to improve recognition rate of speech recognition system, overestimation parameter is used. Finally, with the addition of energy and cepstral mean and variance normalization to features of speech, recognition rate has improved considerably in comparison to standard features and other correlation-based methods. The proposed methods are tested on the Aurora 2 database. Between different proposed methods, autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization (ANSSOEMV) method has a best recognition rate improvement in average than MFCC features which is 64.91% on the Aurora 2 database.

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Section: Introductionmentioning

confidence: 99%

Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition

Farahani

2017

J AUDIO SPEECH MUSIC PROC.

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Noise Robust Speech Recognition Using Deep Belief Networks

Farahat

Halavati

2016

Int. J. Comp. Intel. Appl.

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Most current speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. In these systems acoustic inputs are represented by Mel Frequency Cepstral Coefficients temporal spectrogram known as frames. But MFCC is not robust to noise. Consequently, with different train and test conditions the accuracy of speech recognition systems decreases. On the other hand, using MFCCs of larger window of frames in GMMs needs more computational power. In this paper, Deep Belief Networks (DBNs) are used to extract discriminative information from larger window of frames. Nonlinear transformations lead to high-order and low-dimensional features which are robust to variation of input speech. Multiple speaker isolated word recognition tasks with 100 and 200 words in clean and noisy environments has been used to test this method. The experimental results indicate that this new method of feature encoding result in much better word recognition accuracy.

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Autocorrelation-based Methods for Noise-Robust Speech Recognition

Cited by 2 publications

References 41 publications

Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition

Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition

Noise Robust Speech Recognition Using Deep Belief Networks

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