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New features using robust MVDR spectrum of filtered autocorrelation sequence for robust speech recognition
Sanaz Seyedin1, Seyed Mohammad Ahadi2, Saeed Gazor1
1Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada K7L 3N6.
Thescientificworldjournal
|February 7, 2014
Summary
A new speech recognition method uses a robust perceptual minimum variance distortionless response (MVDR) spectrum to significantly reduce noise. This novel approach enhances feature extraction, outperforming existing methods in various noisy conditions.
Area of Science:
- Speech Recognition
- Signal Processing
- Acoustics
Background:
- Additive noise significantly degrades speech recognition performance.
- Existing feature extraction methods struggle with non-stationary noise residuals.
Purpose of the Study:
- To develop a novel noise-robust feature extraction method for speech recognition.
- To improve robustness against non-stationary additive noise.
Main Methods:
- Utilized the robust perceptual minimum variance distortionless response (MVDR) spectrum of a temporally filtered autocorrelation sequence.
- Modified the distortionless constraint of MVDR spectral estimation using revised subband power spectrum weighting based on signal-to-noise ratios (SNRs).
Main Results:
- The proposed method effectively reduces noise residuals in the estimated spectrum.
- Achieved superior performance compared to Mel frequency cepstral coefficients (MFCC), relative autocorrelation sequence MFCC (RAS-MFCC), and other MVDR-based features on the Aurora 2 task.
- Demonstrated enhanced robustness across various noisy conditions.
Conclusions:
- The novel feature extraction method provides significant noise robustness for speech recognition.
- The modified MVDR approach offers a more effective way to handle non-stationary noise in speech signals.
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