Quality evaluation of speech enhancement algorithms for normal and hearing loss listeners

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The subjective quality test of the enhanced speech from different enhancement algorithms for listeners with normal hearing (NH) capability as well as listeners with hearing impairment (HI) is reported. The subjective quality evaluation of speech enhancement methods in the literature survey is mostly done targeting NH listeners and fewer attempts are observed to subjectively evaluate for HI listeners. The algorithms evaluated are from four different classes: spectral subtraction class(SS), statistical model based class (minimum mean square error), subspace class(PKLT) and auditory class (ideal binary mask using STFT, ideal binary mask using gammatone filterbank and ideal binary mask using gammachirp filterbank). The algorithms are evaluated using four types of real world noises recorded in Indian scenarios namely cafeteria, traffic, station and train at-5, 0, 5 and 10 dB SNRs. The evaluation is being done as per ITU-T P.835 standard in terms of three parameters-speech signal alone, background noise and overall quality. The noisy speech database developed in Indian regional language, Marathi, at four SNRs-5, 0, 5 and 10 dB is used for evaluation. Significant improvement is observed in ideal binary mask algorithm in terms of overall quality and signal distortion ratings for NH and HI listeners. The performance of minimum mean square error is also observed comparable with the ideal binary mask algorithm in some cases.




Shinde, H., Sapkal, A. M., & Phatak, A. (2019). Quality evaluation of speech enhancement algorithms for normal and hearing loss listeners. International Journal of Innovative Technology and Exploring Engineering, 8(12), 7–12. https://doi.org/10.35940/ijitee.L2479.1081219

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