Subjective and objective analysis of Speech Enhancement algorithms for Single Channel Speech Patterns of Indian & English Languages (original) (raw)

This paper presents a comparative study among the seven single channel speech enhancement techniques such as spectral subtraction, Wiener filtering, minimum mean square error under speech presence uncertainty (MMSE-SPU), p-MMSE, log-MMSE and modulation channel selection (MCS). For the investigation of the capability of these techniques, 12 different practical noises on five different language databases were used. The result was analysed based on subjective and objective measure. In subjective measure SNR, peak signal-to-noise ratio (PSNR), segmental-SNR (SSNR) and mean square error (MSE) were considered, whereas for objective measure speech the intelligibility index was taken. The different language (Hindi, Kannada, Malayalam, Bengali and English) databases were taken from the Noizeus speech corpus and IIIT-H Indic speech database, while the noise database was obtained from the Noizex-92 noise corpus. The algorithms were implemented in MATLAB. The results obtained are very encouraging and helpful in the selection of single channel speech enhancement technique for practical application-based noise reduction. Further, among all the mentioned methods, MCS shows overall better performance for the five languages and 12 different practical noises.