There are many factors that can affect the users' quality of experience (QoE) in Voice over IP (VoIP) services, especially in wireless networks. In speech communication systems there are different impairment factors because physical phenomena that occur in the wired and wireless transmission channel. In this context, a non-intrusive speech quality classifier based on Discriminative Restricted Boltzmann Machines (DRBM) is proposed, which considers speech signals degradations caused by wired and wireless network degradations. To accomplish this goal, a test scenario is implemented, in which the speech signal is coded by the AMR-WB and transmitted using both a lossy wired channel and the Rayleigh fading model. Also, different modulation schemes and channel degradations, such as packet loss rate, signal-to-noise ratio and Doppler shifts are implemented. As a result, a speech database is built that is used to train different machine learning algorithms. Experimental results demonstrated that the DRBM reached the best results. Performance assessment results show that the proposed classifier based on DRBM overcomes the current standardized algorithm described in ITU-T Rec. P.563.