Speech intelligibility improvement in noisy reverberant environments based on speech enhancement and inverse filtering

Dong, Huan-Yu; Lee, Chang-Myung

doi:10.1186/s13636-018-0126-8

Cited by 16 publications

(9 citation statements)

References 28 publications

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“…And in the future, it is logical to consider the combination of noise and reverberation as interfering factors. It is possible that the results obtained in [19,20] can serve as a starting point for such studies.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Speech Quality and Intelligibility Assessments in University Classrooms

Prodeus

Kukharicheva

Didkovska

2021

Int. J. Archit. Eng. Technol.

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Estimates of speech quality and intelligibility for three university classrooms of small, medium and large sizes are presented. The quality and intelligibility of speech were assessed by objective methods using binaural room impulse responses, measured at 5-6 points of the premises. The measures of speech quality were log-spectral distortion (LSD), bark spectral distortion (BSD) and perceptual evaluation of speech quality (PESQ), and the objective measure of speech intelligibility was the speech transmission index (STI). Among the quality measures considered, only BSD is shown to be highly correlated with STI measures for all three classrooms. In this case, correlation coefficient R varies from minus 0.6 for a small room to minus 0.98 for a large room. The close relationship between PESQ and STI is observed only in the case of a large classroom (R = 0.96-0.99), and the LSD measure was found to be uncorrelated with STI for premises of all sizes. The obtained results can serve as a justification for the use of BSD instead of STI, and vice versa, in the acoustic examination of classrooms of different sizes.

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Section: Discussionmentioning

confidence: 99%

Comparison of Speech Quality and Intelligibility Assessments in University Classrooms

Prodeus

Kukharicheva

Didkovska

2021

Int. J. Archit. Eng. Technol.

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“…A relatively advanced method was proposed by Dong and Lee in [ 54 ]—it is a combination of the perceptual distortion measure–based speech enhancement (PDMSE) method and the fast inverse filtering (FIF) method. The results are promising, but the authors note that currently, this method does not allow for in-real-time operation.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Sensing Analytics Applied to Speech in Reverberation Conditions

Odya

Kotus

Kurowski

et al. 2021

Sensors

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The paper aims to discuss a case study of sensing analytics and technology in acoustics when applied to reverberation conditions. Reverberation is one of the issues that makes speech in indoor spaces challenging to understand. This problem is particularly critical in large spaces with few absorbing or diffusing surfaces. One of the natural remedies to improve speech intelligibility in such conditions may be achieved through speaking slowly. It is possible to use algorithms that reduce the rate of speech (RoS) in real time. Therefore, the study aims to find recommended values of RoS in the context of STI (speech transmission index) in different acoustic environments. In the experiments, speech intelligibility for six impulse responses recorded in spaces with different STIs is investigated using a sentence test (for the Polish language). Fifteen subjects with normal hearing participated in these tests. The results of the analytical analysis enabled us to propose a curve specifying the maximum RoS values translating into understandable speech under given acoustic conditions. This curve can be used in speech processing control technology as well as compressive reverse acoustic sensing.

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“…The varying speech signal may damage the desired signal and degrade the denoising performance of the adaptive algorithm. Some researchers [29] employed the voice activity detection (VAD) method to make the algorithms only to be adjusted in speech-absent frames and avoid the affection of a varying speech signal. On the other hand, the VAD method is not always practical, especially when the background noise is heavy or the sound field is complex.…”

Section: Introductionmentioning

confidence: 99%

Modified GSC Method to Reduce the Distortion of the Enhanced Speech Signal Using Cross-Correlation and Sidelobe Neutralization

Lee

2021

Applied Sciences

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The generalized sidelobe canceller (GSC) method is a common algorithm to enhance audio signals using a microphone array. Distortion of the enhanced audio signal consists of two parts: the residual acoustic noise and the distortion of the desired audio signal, which means that the desired audio signal is damaged. This paper proposes a modified GSC method to reduce both kinds of distortion when the desired audio signal is a non-stationary speech signal. First, the cross-correlation coefficient between the canceling signal and the error signal of the least mean square (LMS) algorithm was added to the adaptive process of the GSC method to reduce the distortion of the enhanced signal while the energy of the desired signal frame was increased suddenly. The sidelobe pattern of beamforming was then presented to estimate the noise signal in the beamforming output signal of the GSC method. The noise component of the beamforming output signal was decreased by subtracting the estimated noise signal to improve the denoising performance of the GSC method. Finally, the GSC-SN-MCC method was proposed by merging the above two methods. The experiment was performed in an anechoic chamber to validate the proposed method in various SNR conditions. Furthermore, the simulated calculation with inaccurate noise directions was conducted based on the experiment data to inspect the robustness of the proposed method to the error of the estimated noise direction. The experiment data and calculation results indicated that the proposed method could reduce the distortion effectively under various SNR conditions and would not cause more distortion if the estimated noise direction is far from the actual noise direction.

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Speech intelligibility improvement in noisy reverberant environments based on speech enhancement and inverse filtering

Cited by 16 publications

References 28 publications

Comparison of Speech Quality and Intelligibility Assessments in University Classrooms

Comparison of Speech Quality and Intelligibility Assessments in University Classrooms

Acoustic Sensing Analytics Applied to Speech in Reverberation Conditions

Modified GSC Method to Reduce the Distortion of the Enhanced Speech Signal Using Cross-Correlation and Sidelobe Neutralization

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