Speech enhancement using Kalman Filter for white, random and color noise

In this paper, we investigated the enhancement of speech by applying kalman filter. Noise removal is very important in many applications like telephone conversation, speech recognition, etc. The corruption of speech due to presence of additive background noise causes severe difficulties in various communication environments. If the background noise is evolving more slowly than the speech, i.e., if the noise is more stationary than the speech, it is easy to estimate the noise during the pauses in speech. If the Noise is varying rapidly then estimation is more difficult. In this work we used the Kalman filter which is an efficient recursive filter that estimates the internal state of a linear dynamic system from a series of noisy measurements. Compared the results of kalman filter with spectral substraction, weiner filter and found kalman filter has shown good improvement in SNR values.