Blind Estimation of the Speech Transmission Index for Speech Quality Prediction

Prem Seetharaman; Gautham J. Mysore; Paris Smaragdis; Bryan A Pardo

doi:10.1109/ICASSP.2018.8461827

Blind Estimation of the Speech Transmission Index for Speech Quality Prediction

Prem Seetharaman, Gautham J. Mysore, Paris Smaragdis, Bryan A Pardo

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 Scopus citations

Abstract

The speech transmission index (STI) of a listening position within a given room indicates the quality and intelligibility of speech uttered in that room. The measure is very reliable for predicting speech intelligibility in many room conditions but requires an STI measurement of the impulse response for the room. We present a method for blindly estimating the STI without measuring or modeling the impulse response of the room using deep convolutional neural networks. Our model is trained entirely using simulated room impulse responses combined with clean speech examples from the DAPS dataset [1] and works directly on PCM audio. Our experiments show that our method predicts true STI with a high degree of accuracy-an average error of under 4%. It can also distinguish between different STI conditions to a level of granularity that is comparable to humans.

Original language	English (US)
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	591-595
Number of pages	5
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/ICASSP.2018.8461827
State	Published - Sep 10 2018
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: Apr 15 2018 → Apr 20 2018

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2018-April
ISSN (Print)	1520-6149

Other

Other	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	4/15/18 → 4/20/18

Funding

This work was done while on an Adobe Research internship. Paris Smaragdis was supported through NSF grant #1451380.

Keywords

Speech enhancement
Speech quality
Speech transmission index

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2018.8461827

Cite this

Seetharaman, P., Mysore, G. J., Smaragdis, P., & Pardo, B. A. (2018). Blind Estimation of the Speech Transmission Index for Speech Quality Prediction. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 591-595). Article 8461827 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8461827

Seetharaman, Prem ; Mysore, Gautham J. ; Smaragdis, Paris et al. / Blind Estimation of the Speech Transmission Index for Speech Quality Prediction. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 591-595 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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abstract = "The speech transmission index (STI) of a listening position within a given room indicates the quality and intelligibility of speech uttered in that room. The measure is very reliable for predicting speech intelligibility in many room conditions but requires an STI measurement of the impulse response for the room. We present a method for blindly estimating the STI without measuring or modeling the impulse response of the room using deep convolutional neural networks. Our model is trained entirely using simulated room impulse responses combined with clean speech examples from the DAPS dataset [1] and works directly on PCM audio. Our experiments show that our method predicts true STI with a high degree of accuracy-an average error of under 4\%. It can also distinguish between different STI conditions to a level of granularity that is comparable to humans.",

keywords = "Speech enhancement, Speech quality, Speech transmission index",

author = "Prem Seetharaman and Mysore, \{Gautham J.\} and Paris Smaragdis and Pardo, \{Bryan A\}",

note = "Funding Information: This work was done while on an Adobe Research internship. Paris Smaragdis was supported through NSF grant \#1451380.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",

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Seetharaman, P, Mysore, GJ, Smaragdis, P & Pardo, BA 2018, Blind Estimation of the Speech Transmission Index for Speech Quality Prediction. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8461827, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, Institute of Electrical and Electronics Engineers Inc., pp. 591-595, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 4/15/18. https://doi.org/10.1109/ICASSP.2018.8461827

Blind Estimation of the Speech Transmission Index for Speech Quality Prediction. / Seetharaman, Prem; Mysore, Gautham J.; Smaragdis, Paris et al.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 591-595 8461827 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - The speech transmission index (STI) of a listening position within a given room indicates the quality and intelligibility of speech uttered in that room. The measure is very reliable for predicting speech intelligibility in many room conditions but requires an STI measurement of the impulse response for the room. We present a method for blindly estimating the STI without measuring or modeling the impulse response of the room using deep convolutional neural networks. Our model is trained entirely using simulated room impulse responses combined with clean speech examples from the DAPS dataset [1] and works directly on PCM audio. Our experiments show that our method predicts true STI with a high degree of accuracy-an average error of under 4%. It can also distinguish between different STI conditions to a level of granularity that is comparable to humans.

AB - The speech transmission index (STI) of a listening position within a given room indicates the quality and intelligibility of speech uttered in that room. The measure is very reliable for predicting speech intelligibility in many room conditions but requires an STI measurement of the impulse response for the room. We present a method for blindly estimating the STI without measuring or modeling the impulse response of the room using deep convolutional neural networks. Our model is trained entirely using simulated room impulse responses combined with clean speech examples from the DAPS dataset [1] and works directly on PCM audio. Our experiments show that our method predicts true STI with a high degree of accuracy-an average error of under 4%. It can also distinguish between different STI conditions to a level of granularity that is comparable to humans.

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Seetharaman P, Mysore GJ, Smaragdis P, Pardo BA. Blind Estimation of the Speech Transmission Index for Speech Quality Prediction. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 591-595. 8461827. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2018.8461827

Blind Estimation of the Speech Transmission Index for Speech Quality Prediction

Abstract

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Keywords

ASJC Scopus subject areas

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