Impact of University Classroom Size on the Relationship between Speech Quality and Intelligibility


  • Arkadiy Prodeus
  • Maryna Didkovska
  • Kateryna Kukharicheva



binaural room impulse response, speech quality, speech intelligibility, objective measure


In this paper, five objective measures of the quality of speech signals distorted by reverberation are compared with the Speech Transmission Index (STI). The main aim of the comparison is to further test and explain the reasons for the previously discovered phenomenon of an increase in the speech quality and intelligibility with increasing room size. The comparison is performed for three university classrooms of small, medium and large sizes. The correlation coefficients between the quality and intelligibility estimates of speech obtained for 5-6 points of each room are estimated. Speech signal quality is assessed using intrusive measures such as segmental signal-to-noise ratio (SSNR), log-spectral distortion (LSD), frequency-weighted segmental signal-to-noise ratio (FWSNR), bark spectral distortion (BSD), and perceptual evaluation of speech quality (PESQ). For BSD, high correlation coefficients (0.57-0.99) are determined for rooms of all sizes and an increase in the correlation coefficient with the room size increase is found, which can be explained by a decrease in the density of early sound reflections. For FWSNR, high correlation (0.65-0.98) is determined for medium and large rooms. For PESQ, high correlation (0.96-0.99) is obtained for large classroom. SSNR and LSD are found to be uncorrelated with STI for rooms of all sizes.


H. Kuttruff, Room Acoustics, fifth ed., Spon Press, London and New York, 2009, 374 p.

ISO 3382-1:2009. Acoustics – Measurement of room acoustic parameters – Part 1: Performance spaces. Available at:

P. Naylor, N. Gaubitch (Eds.), Speech Dereverberation, Springer-Verlag, London, 2010, 388 p.

W. Yang, J. Bradley, “Effects of room acoustics on the intelligibility of speech in classrooms,” Journal of the Acoustical Society of America, vol. 125, no. 2, pp. 1-12, 2009.

Y. Hu, K. Kokkinakis, “Effects of early and late reflections on intelligibility of reverberated speech by cochlear implant listeners,” Journal of the Acoustical Society of America, vol. 135, no. 1, pp. EL22–EL28, 2013.

A. Prodeus, M. Didkovska, “Assessment of speech intelligibility in university lecture rooms of different sizes using objective and subjective methods,” Eastern-European Journal of Enterprise Technologies, vol. 3, no. 5(111), pp. 47–56, 2021.

J. Lochner, J. Burger, “The influence of reflections on auditorium acoustics,” J. Sound Vib., vol. 1, issue 4, pp. 426–454, 1964.

I. Arweiler, J. Buchholz, T. Dau, “Speech intelligibility enhancement by early reflections,” Proceedings of the International Symposium on Auditory and Audiological Research, Elsinore, Denmark, 2009, vol. 2, pp. 289-298. Available at:

A. Prodeus, K. Kukharicheva, M. Didkovska, “Comparison of speech quality and intelligibility assessments in university classrooms,” Int. J. Archit. Eng. Technol., vol. 8, pp. 52-60, 2021.

G. Fant, Acoustic theory of speech production, The Hague, The Netherlands, Mouton, 1960, 326 p.

J. Flanagan Speech communication. In: Crocker M.J. (ed.), Encyclopedia of Acoustics. John Wiley, New York, 1997, 2017 p.

N. Cote, Integral and Diagnostic Intrusive Prediction of Speech. Springer-Verlag Berlin Heidelberg, 2011, 267 p.

P. Loizou, Speech Enhancement: Theory and Practice, second ed., Boca Raton: CRC Press, Taylor & Francis Group, 2013, 705 p.

S. Young, G. Evermann, M. Gales, et al. The HTK Book. Cambridge: University Engineering Department, 2009, 355 p. Available at:

Y. Tang, C. Arnold, T. Cox, “A study on the relationship between the intelligibility and quality of algorithmically-modified speech for normal hearing listeners,” J. Otorhinolaryngol. Hear. Balance Med., vol. 1, no. 5, pp. 1-10, 2018.

X. Xu, R. Flynn, M. Russell, “Speech intelligibility and quality: A comparative study of speech enhancement algorithms,” Proc. 28th Irish Signals and Systems Conference (ISSC), June 20-21, 2017, pp. 1-6,

M. Keshavarzia, “Comparison of effects on subjective intelligibility and quality of speech in babble for two algorithms: A deep recurrent neural network and spectral subtraction,” The Journal of the Acoustical Society of America, vol. 145, no. 3, pp. 1493–1503, 2019, pp. 1-5.

J. Ma, Y. Hu, P. Loizou, “Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions,” J. Acoust. Soc. Am., vol. 125, no. 5, pp. 3387-3405, 2009.

C. Nestoras, S. Dance, “The interrelationship between room acoustics parameters as measured in university classrooms using four source configurations,” Building Acoustics, vol. 20, no. 1, pp. 43–54, 2013.

M. Jeub, M. Schäfer, P. Vary, “A binaural room impulse response database for the evaluation of dereverberation algorithms,” Int. Conf. Proc. on Digital Signal Processing (DSP), Santorini, Greece, July 5-7, 2009, pp. 1-5.

Perceptual Evaluation of Speech Quality (PESQ) ITU-T Recommendations P.862, P.862.1, P.862.2. Version 2.0. October 2005.

H. J. M. Steeneken, T. Houtgast, “Validation of the revised STIr method,” Elsevier Speech Communication, vol. 38, pp. 26-37, 2002.

D. R. Cox, D. V. Hinkley, Theoretical Statistics. Chapman and Hall/CRC, 1974, 528 p.

E. Kandel, T. Jessell, J. Schwartz, S. Siegelbaum, A. Hudspeth, Principles of Neural Science, fifth ed., A. Hudspeth (ed.). McGraw-Hill, New York, 2013, 451 p. Available at:

J. Beerends, S. Wijngaarden, R. Buuren, “Extension of ITU-T Recommendation P.862 PESQ towards Measuring Speech Intelligibility with Vocoders. In New Directions for Improving Audio Effectiveness,” Proceedings of the RTO-MP-HFM-123, Neuilly-sur-Seine, France, 2005, pp. 10-1–10-6.

A. Prodeus, M. Didkovska, K. Kukharicheva, D. Motorniuk, “Modeling the influence of early sound reflections on speech intelligibility,” Proceedings of the 2020 IEEE 6th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC), Kyiv, Ukraine, October 20-23, 2020, pp. 47-50.

S. Naida, V. Didkovskyi, O. Pavlenko, N. Naida, “Spectral analysis of sounds by acoustic hearing analyzer,” Proceedings of the IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), Kyiv, Ukraine, April 16-18, 2019, pp. 421-424.

S. Naida, V. Didkovskyi, O. Pavlenko, N. Naida, “Objective audiometry based on the formula of the middle ear parameter: A new technique for researches and differential diagnosis of hearing,” Proceedings of the 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), Kyiv, Ukraine, April 16-18, 2019.




How to Cite

Prodeus, A., Didkovska, M., & Kukharicheva, K. (2022). Impact of University Classroom Size on the Relationship between Speech Quality and Intelligibility. International Journal of Computing, 21(3), 342-352.