How to remove salt & pepper noise from speech signal?
If you are forced to use the Discrete Haar Transform, then note that the Discrete Haar Mother Wavelet is nothing but a [1 -1], or in other words, a discrete differentiator, as shown below:



As such, it can/will severely accentuate sudden transitions in your waveform, ie, salt and pepper noise. 

Note, that the first dyadic scale of the DHT will be composed of coefficients computed from the signal correlated with the Haar Mother wavelet. At this level, salt&amp;pepper noise will correlate very well, and will give you large wavelet co-efficients in the wavelet space. One can then simply null the large values out, and then perform an inverse Discrete Haar Wavelet Transform, (IDHT). 

It is strange that you would be required to use the Discrete Haar Wavelet Transform, (over, say, the Stationary Wavelet Transform), because nulling co-efficients in the DWT space and inverse transforming can lead to aliasing. This effect might be small for the problem you describe however. 

Another possibility would be to simply perform the DWT, identify the large wavelet co-efficients within the last half of the DWT transform, (corresponding to high correlations between SP noise and the Haar Mother Wavelet), identify those large co-efficients, and simply null them out in the time domain, without doing an inversion. This will get around the problem of aliasing in the inverse transform.