Binaural Speech Intelligibility Prediction in the Presence of Multiple Babble Interferers Based on Mutual Information

Geravanchizadeh M, Avanaki HJ, Dadvar P. Binaural Speech Intelligibility Prediction in the Presence of Multiple Babble Interferers Based on Mutual Information. J Audio Eng Soc. 2017;65(4):285-292. doi:10.17743/jaes.2017.0004

@article{Geravanchizadeh2017_18561,
  author = {Geravanchizadeh, Masoud and Avanaki, Hadi Jamshidi and Dadvar, Paria},
  title = {{Binaural Speech Intelligibility Prediction in the Presence of Multiple Babble Interferers Based on Mutual Information}},
  journal = {Journal of the Audio Engineering Society},
  volume = {65},
  number = {4},
  pages = {285--292},
  year = {2017},
  month = apr,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2017.0004},
  url = {https://doi.org/10.17743/jaes.2017.0004}
}

TY  - JOUR
TI  - Binaural Speech Intelligibility Prediction in the Presence of Multiple Babble Interferers Based on Mutual Information
AU  - Geravanchizadeh, Masoud
AU  - Avanaki, Hadi Jamshidi
AU  - Dadvar, Paria
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 65
IS  - 4
SP  - 285
EP  - 292
PY  - 2017
DA  - 2017/04/06
DO  - 10.17743/jaes.2017.0004
UR  - https://doi.org/10.17743/jaes.2017.0004
PB  - Audio Engineering Society
LA  - en
AB  - This paper describes a predictor for binaural speech intelligibility that computes speech reception thresholds (SRT) without the need to perform subjective listening tests. Although listening tests are considered to be the most reliable indicators of performance, such tests are time consuming and costly. The proposed model computes SRTs in two stages. First, it calculates the binaural advantage. Then, it derives the SRTs based on the computed mutual information of the speech and mixture envelopes. Listening tests were conducted with 13 normal-hearing listeners in 15 spatial configurations, covering one, two, and three babble interferers. The proposed predictor performs as well as the baseline model in predicting the intelligibility of binaural vowel-consonant-vowel signals contaminated by multiple nonstationary babble noise sources. The model is evaluated in anechoic conditions and compared with subjective data as well as with the predictions obtained from a baseline binaural speech intelligibility model.
ER  -