Research Article | OPEN ACCESS
Speech Intelligibility Prediction Intended for State-of-the-Art Noise Estimation Algorithms
1Nasir Saleem, 2Sher Ali, 1Ehtasham Mustafa and 3Usman Khan
1Institute of Engineering and Technology, GU, D.I. Khan, KPK, Pakistan
2City University of Science and Technology, Peshawar, KPK, Pakistan
3University of Engineering and Technology, Kohat Campus, Kohat, KPK, Pakistan
Research Journal of Applied Sciences, Engineering and Technology 2014 2:296-302
Received: April 05, 2013 | Accepted: April 29, 2013 | Published: January 10, 2014
Abstract
Noise estimation is critical factor of any speech enhancement system. In presence of additive non-stationary background noise, it is difficult to understand speech for normal hearing particularly for hearing impaired person. The background interfering noise reduces the intelligibility and perceptual quality of speech. Speech enhancement with various noise estimation techniques attempts to minimize the interfering components and enhance the intelligibility and perceptual aspects of damaged speech. This study addresses the selection of right noise estimation algorithm in speech enhancement system for intelligent hearing. A noisy environment of airport is considered. The clean speech is corrupted by noisy environment for different noise levels ranging from 0 to 15 dB. Six diverse noise estimation algorithms are selected to estimate the noise including Minimum Controlled Recursive Average (MCRA), MCRA-2, improved MCRA, Martin minimum tracking, continuous spectral minimum tracking, and weighted spectral average. Spectral subtraction algorithm is used for enhancing the noisy speech. The intelligibility of enhanced speech is assessed by the fractional Articulation Index (fAI) and SNRLOSS.
Keywords:
fAI, IMCRA, MCRA, MCRA-2, noise estimate, SNRLOSS, spectral subtraction,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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The authors have no competing interests.
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