On Real-Time Mean-and-Variance Normalization of Speech Recognition Features

Pujol, Pere; Macho, Dušan; Nadeu, Climent

doi:10.1109/icassp.2006.1660135

Cited by 25 publications

(12 citation statements)

References 4 publications

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“…By using the center positions and the measured angle, both eyes are aligned in the face image. In addition, each image is resized to 60ˆ60, and following this step the resized image undergoes histogram equalization (HE) and mean-variance normalization (MVN) [38] to reduce the effect of illumination. The facial features are then extracted using Log-Gabor transform.…”

Section: Unimodal Biometric Systemsmentioning

confidence: 99%

Optimal Face-Iris Multimodal Fusion Scheme

Sharifi

Eskandari

2016

Symmetry

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Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA), a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.

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Section: Unimodal Biometric Systemsmentioning

confidence: 99%

Optimal Face-Iris Multimodal Fusion Scheme

Sharifi

Eskandari

2016

Symmetry

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“…Spectral subtraction (SS) [20] is one of the earliest approach to noise compensation and speech enhancement, used for the suppression of additive noise from the corrupt signal. It is based on method of subtracting the noise estimate (magnitude) from the corrupt spectrum assuming noise to be stationary.…”

Section: Resultsmentioning

confidence: 99%

Performance of Complementary Features for Robust Speaker Identification

Chougule¹,

Chavan²

2015

IJCA

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This paper considers the problem of acoustic mismatch caused by use of different sensors, in digital gazettes and hand-held devices. In this paper, two complementary features derived from conventional cepstral features are proposed, namely linear/mel spectral subband features (L/M-SSC) and log filter bank energy features (LFBE). The performance of these complementary features is compared with conventional features in acoustic mismatch conditions. To investigate the performance of features alone, all processing and classification steps are kept constant to allow a controlled comparison. A multi-variability speech database (IITG-MV) with acoustic mismatch (different microphones) is used for experimental evaluation. It is observed that all these features shows almost equal performance for text independent speaker identification in same acoustic condition. Whereas in mismatch condition, spectral subband centroids (L/M-SSC) features proved to be robust than other features when used alone. Further, use of dynamic features along with channel and noise compensation enhances the percentage identification rate of the system for all cases of acoustic mismatch, with spectral subband centroid features showing comparable performance to that of conventional features. KeywordsMFCC, LFCC, Linear/Mel scale spectral subband centroids (L/M-SSC), Log filter bank energy (LFBE)

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“…Many algorithms have been proposed to deal with this problem which show significant improvement in performance for quasi-stationary noise (e.g. [3], [4], [5], [6], [7]). Unfortunately these same algorithms frequently do not show significant improvements in more difficult transitory environments such as background music (e.g.…”

Section: Introductionmentioning

confidence: 99%

Robust speech recognition using a Small Power Boosting algorithm

Kim

Kumar

Stern

2009

2009 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

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Abstract-In this paper, we present a noise robustness algorithm called Small Power Boosting (SPB). We observe that in the spectral domain, time-frequency bins with smaller power are more affected by additive noise. The conventional way of handling this problem is estimating the noise from the test utterance and doing normalization or subtraction. In our work, in contrast, we intentionally boost the power of time-frequency bins with small energy for both the training and testing datasets. Since timefrequency bins with small power no longer exist after this power boosting, the spectral distortion between the clean and corrupt test sets becomes reduced. This type of small power boosting is also highly related to physiological nonlinearity. We observe that when small power boosting is done, suitable weighting smoothing becomes highly important. Our experimental results indicate that this simple idea is very helpful for very difficult noisy environments such as corruption by background music.

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On Real-Time Mean-and-Variance Normalization of Speech Recognition Features

Cited by 25 publications

References 4 publications

Optimal Face-Iris Multimodal Fusion Scheme

Optimal Face-Iris Multimodal Fusion Scheme

Performance of Complementary Features for Robust Speaker Identification

Robust speech recognition using a Small Power Boosting algorithm

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