This paper presents a non-invasive technique using wavelet speech signal analysis to estimate the degree of severity on a patient with vocal cord disorders. The early symptom is usually characterized by a perceivable hoarse voice that changes with respect to the degree of severity. The first step was to estimate the fundamental frequency and the formant of the speech. Using these features, we were able to measure the distortion using statistical analysis of jitter, shimmer, and harmonicsto-noise ratio (HNR), respectively. From this experiment, prescribed thresholds were set on the distortion features to classify automatically whether a subject was normal, or suffer from a mild or severe. This classification was then validated by the ear-nose throat doctor using the fiber-optic laryngoscope (gold standard technique). The second experiment, we propose sinusoidal modeling synthesis to enhance the speech signal transmitted over wireless network and compared the results to the Daubechies wavelet speech analysis technique. The results of both experiments suggest that the proposed technique may be used as an alternative non-invasive diagnostic technique.