Speech quality estimation is vital to the evaluation of quality of service offered by a telecommunications network. Traditionally, speech quality is estimated using subjective tests. In subjective tests, the quality of a speech signal under test is evaluated by a group of human listeners who assign an opinion score on an integral scale ranging between 1 (bad) to 5 (excellent). The average of these scores, termed the Mean Opinion Score (MOS), is considered as the ultimate determinant of the speech quality [1]. Subjective tests are, however, time consuming and expensive. To make up for these limitations, there has been a growing interest in devising software based objective assessment models. There are two kinds of objective assessment models, namely, intrusive and non-intrusive. Intrusive models evaluate the quality of a distorted speech signal in presence of a corresponding reference signal. The current ITU-T recommendation P.862 (PESQ) is an example of such an approach. Non-intrusive models, on the other hand, do not enjoy this privilege and base their results solely on the estimated features of the signal under test. For this reason, the results of the latter type of models are generally considered inferior to those of the former type.