Bayesian Adaptive Method for Estimating Speech Intelligibility in Noise

Gaubitch ND, Brookes M, Naylor PA, Sharma D. Bayesian Adaptive Method for Estimating Speech Intelligibility in Noise. In: AES Conference: 39th International Conference: Audio Forensics: Practices and Challenges. Audio Engineering Society; 2010. Paper 7-1. Available from: https://aes.org/publications/elibrary-page/?id=15486

@inproceedings{Gaubitch2010_15486,
  author = {Gaubitch, Nikolay D. and Brookes, Mike and Naylor, Patrick A. and Sharma, Dushyant},
  title = {{Bayesian Adaptive Method for Estimating Speech Intelligibility in Noise}},
  booktitle = {AES Conference: 39th International Conference: Audio Forensics: Practices and Challenges},
  note = {Paper 7-1},
  year = {2010},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15486}
}

TY  - CPAPER
TI  - Bayesian Adaptive Method for Estimating Speech Intelligibility in Noise
AU  - Gaubitch, Nikolay D.
AU  - Brookes, Mike
AU  - Naylor, Patrick A.
AU  - Sharma, Dushyant
T2  - AES Conference: 39th International Conference: Audio Forensics: Practices and Challenges
M1  - Paper 7-1
PY  - 2010
DA  - 2010/06/06
UR  - https://aes.org/publications/elibrary-page/?id=15486
PB  - Audio Engineering Society
LA  - en
AB  - We present the Bayesian Adaptive Speech Intelligibility Estimation (BASIE) method - a tool for rapid estimation of a given speech reception threshold (SRT) and the slope at that threshold of multiple psychometric functions for speech intelligibility in noise. The core of this tool is an adaptive Bayesian procedure, which adjusts the signal-to-noise ratio at each subsequent stimulus such that the expected variance of the threshold and slope estimates are minimised. Simulation results show that the algorithm is able to achieve SRT estimates accurate to within +-1 dB in under 30 iterations. Furthermore, we discuss strategies for using BASIE to evaluate the effects of speech processing algorithms on intelligibility and we give two illustrative examples for different noise reduction methods with supporting listening experiments.
ER  -