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

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 SNR_LOSS.