The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
An algorithm that improves speech intelligibility in noise for normal-hearing listeners
Open accessAbstract
Traditional noise-suppression algorithms have been shown to improve speech quality, but not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using the ideal binary mask, an algorithm is proposed that decomposes the input signal into time-frequency (T-F) units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit is dominated by the target or the masker. Speech corrupted at low signal-to-noise ratio (SNR) levels (−5 and 0dB) using different types of maskers is synthesized by this algorithm and presented to normal-hearing listeners for identification. Results indicated substantial improvements in intelligibility (over 60% points in −5dB babble) over that attained by human listeners with unprocessed stimuli. The findings from this study suggest that algorithms that can estimate reliably the SNR in each T-F unit can improve speech intelligibility.
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Cites
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Cited by
Supervised Speech Separation Based on Deep Learning: An Overview
Wang, D., & Chen, J. (2018) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2018.2842159 · Cited by 1,524
Learning Spectral Mapping for Speech Dereverberation and Denoising
Han, K., Wang, Y., Wang, D., Woods, W. S., Merks, I., & Zhang, T. (2015) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2015.2416653 · Cited by 260
Metadata
- Authors
- Kim, G., Lu, Y., Hu, Y., & Loizou, P. C.
- Venue
- The Journal of the Acoustical Society of America
- Publisher
- Acoustical Society of America (ASA)
- Published
- 2009-09-01
- Volume
- 126
- Issue
- 3
- Pages
- 1486-1494
- ISSN
- 0001-4966 (ISSN-L)
- 1520-8524
- OpenAlex
- W1963970749
- Cited by
- 321
- References
- 21
- FWCI
- 27.27
- Type
- journal article
- Language
- en
- Open access
- bronze
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