Correction and Interpolation of Noise Corrupted Voice Using Markov Chain Detection Technique

In this paper, we present a technique for voice correction and interpolation using Markov chain detection. In a communication link, the voice is impaired by spectral distortions generated by the noise and fading. Our purpose is to restore voice samples as close as possible to that of the original one of the speaker using a Markov chain detection technique, which compensates for the spectral distortions. Assuming that, voice maintains high order memory, this has been exploited for restoration of distorted voice samples. By formulating the problem of missing or distorted voice samples by Markov chain Detection Technique, we show that it is possible to correct and interpolate the degraded signal close to that of original one. The performance of the restored signal is evaluated by few simulations using spectrogram analysis.