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