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.
Hu Y, Kokkinakis K. Effects of early and late reflections on intelligibility of reverberated speech by cochlear implant listeners. J Acoust Soc Am. 2013; 135(1): https://doi.org/10.1121/1.4834455
Yang W, Bradley J. Effects of room acoustics on the intelligibility of speech in classrooms. J Acoust Soc Am. 2009; 125(2): 1-12. https://doi.org/10.1121/1.3058900
Bradley J. Review of objective room acoustics measures and future needs. Appl Acoust. 2011; 72(10): 713-720. https://doi.org/10.1016/j.apacoust.2011.04.004
Arweiler I, Buchholz J, Dau T. Speech intelligibility enhancement by early reﬂections. ISAAR 2009: Binaural Processing and Spatial Hearing, 2nd International Symposium on Auditory and Audiological Research, Elsinore, Denmark 2009; Available at: https://proceedings.isaar.eu/index.php/isaarproc/article/view/2009-29
Prodeus A, Didkovska M. Assessment of speech intelligibility in university lecture rooms of different sizes using objective and subjective methods. E-Europ J Enterprise Technol. 2021; 35(111): 47-56. https://doi.org/10.15587/1729-4061.2021.228405
Leccese F, Rocca M, Salvadori G. Fast estimation of Speech Transmission Index using the Reverberation Time: Comparison between predictive equations for educational rooms of different sizes. Appl Acoust. 2018; 140: 143-149. https://doi.org/10.1016/j.apacoust.2018.05.019
Nestoras C, Dance S. The Interrelationship Between Room Acoustics Parameters as Measured in University Classrooms Using Four Source Configurations. Build Acoust. 2013; 20(1): 43-54. https://doi.org/10.1260/1351-010X.20.1.43
Eldakdoky S. Optimizing acoustic conditions for two lecture rooms in Faculty of Agriculture, Cairo University. Ain Shams Eng J. 2017; 8: 481-490. http://dx.doi.org/10.1016/j.asej.2016.08.013
Duran S, Ausiello L, Battaner-Moro J. Acoustic Design Criteria for Higher-Education Learning Environments. Proc Inst Acoust. 2019; 41(3): 1-12. Available at: https://pure.solent.ac.uk/en/publications/acoustic-design-criteria-for-higher-education-learning-environmen
Choi Y-J. The intelligibility of speech in university classrooms during lectures. Appl Acoust. 2020; 162: 107211. https://doi.org/10.1016/j.apacoust.2020.107211
Prodeus A, Didkovska M, Motorniuk D, Dvornyk O. The Effects of Noise, Early and Late Reflactions on Speech Intelligibility. Proc. IEEE 40th Int. Conf. on Electronics and Nanotechnology (ELNANO`2020), Kyiv, Ukraine 2020; 488-492. https://doi.org/10.1109/ELNANO50318.2020.9088854
Prodeus A, Didkovska M. Objective assessment of speech intelligibility in small and medium-sized classrooms. Proc. IEEE Int. Scientific-Practical Conf. on Problems of Infocommunications, Science and Technology (PIC S&T`2020), Kharkiv, Ukraine 2020; Available at: https://www.researchgate.net/publication/347490796_Objective_Assessment_of_Speech_Intelligibility_in_Small_and_Medium-Sized_Classrooms
Aachen Impulse Response Database. Available at: https://www.iks.rwth-aachen.de/en/research/tools-downloads/databases/aachen-impulse-response-database/
Jeub M, Schäfer M, Vary P. A binaural room impulse response database for the evaluation of dereverberation algorithms. Int. Conf. Proc. on Digital Signal Processing (DSP), Santorini, Greece 2009. https://doi.org/10.1109/ICDSP.2009.5201259
Perceptual Evaluation of Speech Quality (PESQ) ITU-T Recommendations P.862, P.862.1, P.862.2. Version 2.0 - October 2005.
Dvornyk O, Motorniuk D, Didkovska M, Prodeus A. Artificial Software Complex "Artificial Head". Part 1. Adjusting the Frequency Response of the Path," Microsystems, Electronics and Acoustics, 2020’ 22(1): https://doi.org/10.20535/2523-4455.mea.198431
Falk T, Zheng C, Chan W-Y. A Non-Intrusive Quality and Intelligibility Measure of Reverberant and Dereverberated Speech. IEEE Transactions on Audio, Speech, and Language Processing, 2010; 18(7): https://doi.org/10.1109/TASL.2010.2052247
Tang Y, Arnold C, Cox TJ. A Study on the Relationship between the Intelligibility and Quality of Algorithmically-Modified Speech for Normal Hearing Listeners. J Otorhinolaryngol Hear Balance Med. 2018; 1(1): 5. https://doi.org/10.3390/ohbm1010005
Dong H, Lee C. Speech intelligibility improvement in noisy reverberant environments based on speech enhancement and inverse filtering. J Audio Speech Music Proc. 2018; 3. https://doi.org/10.1186/s13636-018-0126-8
Prodeus A, Didkovskyi V. Objective estimation of the quality of radical noise suppression algorithms. Radioelectronics and Communications Systems, 2016; 59(11): 502-509. https://doi.org/10.3103/S0735272716110042.
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