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    Abstract
2014 (Vol. 7, Issue: 2)
Article Information:

Speech Intelligibility Prediction Intended for State-of-the-Art Noise Estimation Algorithms

Nasir Saleem, Sher Ali, Ehtasham Mustafa and Usman Khan
Corresponding Author:  Nasir Saleem 

Key words:  fAI, IMCRA, MCRA, MCRA-2, noise estimate, SNRLOSS, spectral subtraction
Vol. 7 , (2): 296-302
Submitted Accepted Published
April 05, 2013 April 29, 2013 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.
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  Cite this Reference:
Nasir Saleem, Sher Ali, Ehtasham Mustafa and Usman Khan, 2014. Speech Intelligibility Prediction Intended for State-of-the-Art Noise Estimation Algorithms.  Research Journal of Applied Sciences, Engineering and Technology, 7(2): 296-302.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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