Speech Intelligibility Based Enhancement System Using Modified Deep Neural Network and Adaptive Multi-band Spectral Subtraction

Dash, Tusar Kanti; Solanki, Sandeep Singh

doi:10.1007/s11277-019-06902-0

“…5. Segmental SNR improvement for Proposed Method for suppression of recorded noise variants including Market Crowd, District Hospital Crowd and University Restaurant noises during testing phase together with comparison of methods (i) AMBSS [19] (ii) PDA [22] (iii) RDNN [12] (iv) MBSS [9], and (v) SOS [4] Fig. 6.…”

Section: Resultsmentioning

confidence: 99%

“…8. STOI score improvement for Proposed Method for suppression of noise variants [26], [27] during multi sub-frame pre-processing and training phase together with comparison of methods (i) AMBSS [19] (ii) PDA [22] (iii) RDNN [12] (iv) MBSS [9], and (v) SOS [4] Fig. 9.…”

Section: Resultsmentioning

confidence: 99%

A Fully Connected Deep Neural Network approach with multiple sub-frame consideration and phase recompense for noise suppression

Nisa¹

2021

Preprint

0

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In the speech communication process, the desirable speech needs to be addressed under the influence of noise encountered in diverse environments that degrade the speech quality and intelligibility. In opposition to the unfavorable scenario particularly lowered signal-to-noiseratio, the progress of traditional noise suppressive algorithms is hindered, introducing further distortion in speech, making them non-applicable for real-time applications. In order to reduce the complicacies of current algorithms, a hybrid approach for upgrading the quality together with intelligibility of speech is proposed for dealing with real-world hearing scenario. For improving the intelligibility of speech of interest, multiple sub-frame analysis using over-spectral subtractive factor with phase recompense approach is implemented on the multi-channel noise corrupted speech, yielding approximated speech spectrum, that constitutes the pre-processing stage. The approximated speech spectrum and clean speech spectrum forming the training set are further fed to Fully Connected Layered Deep Neural Network to reduce the mean square error with the incorporation of regression network resulting in improved quality for speech. The proposed hybrid network results in upgraded intelligibility and quality in speech signal with improved SNR measured in terms of Short-Time-Objective-Intelligibility (STOI) score, Perceptual-Evaluation-of-Speech-Quality (PESQ) score, Segmental SNR level, and Mean Square Error (MSE) in contrast to prior noise suppressive algorithms together with less complexity of the hybrid network.<br>

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“…The mean square computed during training and testing phases are pictured in figures 10 and 11 respectively. [19] (ii) PDA [22] (iii) RDNN [12] (iv) MBSS [9], and (v) SOS [4] Fig. 5.…”

Section: Resultsmentioning

confidence: 99%

“…7. PESQ score improvement for Proposed Method for suppression of recorded noise variants including Market Crowd, District Hospital Crowd and University Restaurant noises during testing phase together with comparison of methods (i) AMBSS [19] (ii) PDA [22] (iii) RDNN [12] (iv) MBSS [9], and (v) SOS [4] Fig. 8.…”

Section: Resultsmentioning

confidence: 99%

“…The architecture is designed for reduced computation burden applicable for real-time scenarios particularly for smartphonefocused biaural listening kits. Dash and Solanki [19] have put forward a hybrid methodology for upgrading the quality and intelligibility of speech. the author incorporates Adaptive approach to multiband spectral subtraction together with phase details for upgrading the intelligibility.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Fully Connected Deep Neural Network approach with multiple sub-frame consideration and phase recompense for noise suppression

Nisa¹

2021

Preprint

0

View full text Add to dashboard Cite

In the speech communication process, the desirable speech needs to be addressed under the influence of noise encountered in diverse environments that degrade the speech quality and intelligibility. In opposition to the unfavorable scenario particularly lowered signal-to-noiseratio, the progress of traditional noise suppressive algorithms is hindered, introducing further distortion in speech, making them non-applicable for real-time applications. In order to reduce the complicacies of current algorithms, a hybrid approach for upgrading the quality together with intelligibility of speech is proposed for dealing with real-world hearing scenario. For improving the intelligibility of speech of interest, multiple sub-frame analysis using over-spectral subtractive factor with phase recompense approach is implemented on the multi-channel noise corrupted speech, yielding approximated speech spectrum, that constitutes the pre-processing stage. The approximated speech spectrum and clean speech spectrum forming the training set are further fed to Fully Connected Layered Deep Neural Network to reduce the mean square error with the incorporation of regression network resulting in improved quality for speech. The proposed hybrid network results in upgraded intelligibility and quality in speech signal with improved SNR measured in terms of Short-Time-Objective-Intelligibility (STOI) score, Perceptual-Evaluation-of-Speech-Quality (PESQ) score, Segmental SNR level, and Mean Square Error (MSE) in contrast to prior noise suppressive algorithms together with less complexity of the hybrid network.<br>

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Improved Spectral Subtraction Method for Civil Aircraft Approach and Landing Crew Interactive Voice Enhancement

Chen

¹

,

Ning

²

,

Man

³

et al. 2022

Engineering Psychology and Cognitive Ergonomics

1

0

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Speech Intelligibility Based Enhancement System Using Modified Deep Neural Network and Adaptive Multi-band Spectral Subtraction

Cited by 12 publications

References 36 publications

A Fully Connected Deep Neural Network approach with multiple sub-frame consideration and phase recompense for noise suppression

A Fully Connected Deep Neural Network approach with multiple sub-frame consideration and phase recompense for noise suppression

A Fully Connected Deep Neural Network approach with multiple sub-frame consideration and phase recompense for noise suppression

Improved Spectral Subtraction Method for Civil Aircraft Approach and Landing Crew Interactive Voice Enhancement

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