Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions.
The articulation index (AI), speech-transmission index (STI), and coherence-based intelligibility metrics have been evaluated primarily in steady-state noisy conditions and have not been tested extensively in fluctuating noise conditions. The aim of the present work is to evaluate the performance of new speech-based STI measures, modified coherence-based measures, and AI-based measures operating on short-term (30 ms) intervals in realistic noisy conditions. Much emphasis is placed on the design of new band-importance weighting functions which can be used in situations wherein speech is corrupted by fluctuating maskers. The proposed measures were evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech (consonants and sentences) corrupted by four different maskers (car, babble, train, and street interferences). Of all the measures considered, the modified coherence-based measures and speech-based STI measures incorporating signal-specific band-importance functions yielded the highest correlations (r=0.89-0.94). The modified coherence measure, in particular, that only included vowel/consonant transitions and weak consonant information yielded the highest correlation (r=0.94) with sentence recognition scores. The results from this study clearly suggest that the traditional AI and STI indices could benefit from the use of the proposed signal- and segment-dependent band-importance functions.
College of Computer Engineering and Software, Taiyuan University of Technology, Shanxi 030024, China.
Jianfen
J
Ma
Yi
Y
Hu
Philipos C
PC
Loizou
D000465:Algorithms; D001336:Automobiles; D004777:Environment; D006801:Humans; D009622:Noise; D010470:Perceptual Masking; D010700:Phonetics; D011890:Railroads; D013018:Sound Spectrography; D013060:Speech; D013065:Speech Intelligibility