Compressed sensing for telehealth assessment of disordered voices

Abstract The goal of this research work is to propose compressed sensing (CS) as a component of a telemedicine system for compressing speech signals in the framework of telehealth assessment of disordered voices. The main challenge is the reconstruction of the compressed medical audio data without affecting the acoustics markers used by the clinicians in the diagnosis and interpretation. Firstly, the speech signal is compressed by applying the CS at the appropriate compression rate, to each frame to reduce the size of the signal. Next, the compressed speech signal is transmitted through a transmission channel. The speech signal is recovered from the CS-based compressed signal using an appropriate algorithm. Finally, the acoustics markers jitter, shimmer, harmonic-to-noise rate (HNR) and cepstral peak prominence (CPP) are computed for disordered voices assessment. The effect of the compression on the acoustic markers is investigated by using a corpus of Spanish sustained vowels /a/ uttered by 79 normophonic and dysphonic speakers with different degrees of dysphonia. The robustness of the proposed method agains background noise is evaluated by corrupting the voice signals with synthetic white Gaussian noise at different levels. Experimental results show that there are no significant differences in the correlations between the auditory evaluation scores and acoustics markers computed from the original speech signal and the recovered signal compressed via CS-based method even at a compression rate (CR) as high as 75%. The performance of the CS-based method in terms of the computed acoustics markers and mean square error (MSE), for a fixed CR, is compared to that of the MP3 and advanced audio coding (AAC) audio compression formats. The CS technique is a very effective tool for compressing speech signals in the framework of telehealth assessment of disordered voices.