Now accepting submissions for the upcoming volume
Electronic and Acoustic Engineering
ISSN 2524-2725 · e‑ISSN 2617-0965 Open Access · CC BY-NC 4.0
Vol. 2 · Issue 3 · 2019 Jun 28, 2019 Acoustical devices and systems

Subjective Assessment of the Intelligibility of Noised Speech in Lecture Room

OA
Oleksii Olehovych Andriichenko Corresponding National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” oleksiy.andriichenko@gmail.com Ukraine
OD
Oleksandr Ihorovych Denysenko National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Ukraine
Pages55-60 PublishedJun 28, 2019 LicenseOpen Access
EAE 3 VOL 2 · 3
VOL 2 · NO 3 · 2019 View issue

Abstract

Subjective assessment of the speech intelligibility is of great practical interest, since this parameter can be used in many engineering and natural-mathematical fields, such as bioengineering, design of lecture rooms and concert halls, mathematical modeling and medicine. By this time, several attempts were made to assess the intelligibility of the room. The results of such experiments became conclusions: 1) early reflections improve the intelligibility of distorted noise and reverb speech; 2) using binaural type of listening we will have better intelligibility than with monoural. But to re-implement such experiments, it is necessary to use a lot of expensive equipment, which is not always possible. Therefore, the purpose of this work is to study the influence of the characteristics of the room on the speech  intelligibility without the use of a large number of equipment. The idea of the experiment is as follows: distorted by noise and reverb signals must be listened to by the participants in the experiment, the perceived sound is fixed and compared with the undistorted signal. The clear signal was recorded using a microphone, sound card and audio file software. Synthesis of distorted signals took place in two stages: 1) adding white noise; 2) filtering the resulting mix through a non-recursive digital filter using the impulse characteristics of the room for six distances to the source of sound. The mathematical model of the distorted (output) signal was the convolution of an additive mix of clear signal and white noise with a pulse characteristic. To ensure the necessary signal-to-noise ratio, the noise was weighed by the corresponding coefficients at the stage of adding it to the clear signal. Testing was carried out in five stages: 1) simulating of distorted signals; 2) voicing signal to participant; 3) signal listening; 4) fixation of the perceived sound; 5) intelligibility calculating. For testing, six binaural impulse characteristics of the room with known parameters from the Aachen database of impulse characteristics were used. Each of the impulse characteristics corresponds to a certain value of the distance from the sound source to the microphone. After the completion of the experiment, the overall result was obtained by averaging by the number of participants. The overall result has shown that for small values of the signal-to-noise ratio over long distances, the intelligibility is greater than for small distances. For high values, the signal-to-noise ratio is better for small distances. Such results may be explained by the fact that, at long distances, the combination of the effects of early reflections and binaural listening positively affects the intelligibility. The main reason for the low intelligibility of the distorted by noise speech in the room is a late reverberation. As a conclusion we can say that the computer modeling method makes it easy to create a distorted by the noise and influence of the room signal; It is shown that, for small values, the signal-to-noise ratio is more readable than for small distances.

Keywords

References

  1. J. Blauert Ed., The technology of binaural listening. Springer, Berlin–Heidelberg–New York, 2013.
  2. J. Lochner and J. Burger, “The influence of reflections on auditorium acoustics,” Journal of Sound and Vibration, No. 1, pp. 426-454, 1964.
  3. G. Soulodre, N. Popplewell, and J. Bradley, “Combined effects of early reflections and background noise on speech intelligibility,” Journal of Sound and Vibration, vol. 135, No.1, pp. 123-133, 1989.
  4. J. Bradley, “Predictors of speech intelligibility in rooms,” J. Acoust. Soc. Am., vol 80, pp. 837–845, 1986.
  5. J. Bradley, “Speech intelligibility studies in classrooms,” J. Acoust. Soc. Am., vol. 80, pp. 846–854, 1986.
  6. J. Bradley, R. Reich, and S. Norcross, “On the combined effects of signal-to-noise ratio and room acoustics on speech intelligibility,” J. Acoust. Soc. Am., vol. 106 (4), Pt. 1, pp. 1820-1828, October 1999.
  7. H Sato and J. Bradley, “Evaluation of acoustical conditions for speech communication in working elementary school classrooms,” J. Acoust. Soc. Am. 106 (4), Pt. 1, pp. 2064–2077, 2004.
  8. J Bradley and H. Sato, “Speech intelligibility test results for grades 1, 3 and 6 children in real classrooms,” Proc. of ICA, Kyoto, 2004.
  9. W. Yang and J. Bradley, “Effects of room acoustics on the intelligibility of speech in classrooms for young children,” J. Acoust. Soc. Am., vol. 125 (2), pp. 922–933, 2009.
  10. A. Prodeus, K. Bukhta, P. Morozko, O. Serhiienko, I. Kotvytskyi, O. Dvornyk, “Automated Subjective Assessment of Speech Intelligibility in Various Listening Modes,” Microsystems, Electronics and Acoustics, vol. 23, no. 3, pp.49-57, 2018.
  11. J. Bradley, H. Sato, and M. Picard, “On the importance of early reflections for speech in rooms,” J. Acoust. Soc. Am., vol. 113, no. 6, pp. 3233-3244, June 2003.
  12. I. Arweiler, J. Buchholz, and T. Dau, “Speech intelligibility enhancement by early reflections,” Proc. of 2nd Int. Symposium on Auditory and Audiological Research (ISAAR 2009), Elsinore, Denmark, August 2009.
  13. S. Naida, O. Pavlenko, “Coupled Circuits Model in Objective Audiometry,” Proc. of the 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO), pp. 281-286, April 24-26, Kyiv, Ukraine, April 2018.
  14. S. Naida, O. Pavlenko, “Newborn Hearing Screening Based on the Formula for the Middle Ear Norm Parameter,” Proc. of the 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO), pp. 287-291, April 24-26, Kyiv, Ukraine, April 2018.
  15. J. Benesty, Y. Huang, and J. Chen, Wiener and Adaptive Filters. In Springer Handbook of Speech Processing, J. Benesty, M. Sondhi, and Y. Huang, Eds. Springer-Verlag Berlin Heidelberg, 2008, pp. 103-120.
  16. E. Habets, N. Gaubitch, and P. Naylor, “Temporal selective dereverberation of noisy speech using one microphone,” Proc. of 2008 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 4577-4580, March-April 2008.
  17. A. Prodeus, K. Bukhta, P. Morozko, O. Serhiienko, I. Kotvytskyi, I. Shherbenko, “Automated System for Subjective Evaluation of the Ukrainian Speech Intelligibility,” Proc. of IEEE 38th Int. Conf. on Electronics and Nanotechnology (ELNANO), pp. 533-538, April 24- 26, Kyiv, Ukraine, 2018.
  18. M. Jeub, M. Schäfer, and P. Vary, “A binaural room impulse response database for the evaluation of dereverberation algorithms,” In Int. Conf. Proc. on Digital Signal Processing (DSP), Santorini, Greece, 2009.
  19. Aachen Impulse Response Database. Available on-line: https://www.iks.rwth-aachen.de/en/research/tools-downloads/databases/aachen-impulse-response-database
  20. W. Ahnert, W. Schmidt, Fundamentals to perform acoustical measurements. Appendix to EASERA. Berlin, 2005.

License

CCBY-NC 4.0
Creative Commons Attribution 4.0 International

This work is openly licensed — share and adapt freely with attribution to the authors and the journal. View license terms ↗

§ 06 — Related

Similar articles in this journal

Related peer-reviewed studies published in this journal.
View all issues

Similar Articles

1-10 of 41

You may also start an advanced similarity search for this article.