In communication services, speech quality plays an important role to achieve user expectations. Nowadays, there are different objective methods to estimate speech quality. Parametric models consider different factors, such as network parameters, acoustic characteristics, communication equipment, among others. The most representative parametric models for telephone service are described in ITU-T Rec. G.107 and G.107.1, mostly known as E-model and WB E-model, respectively. However, they do not consider wireless network parameters as inputs. In this context, this research proposes a speech quality parametric model (SQPM) based on artificial neural networks that considers both wireless network degradation characteristics and the techniques used to improve the transmission quality. For this purpose, a network simulator was built, in which two forward error correction (FEC) codes and four different antenna configurations in a multiple-input-multiple-output (MIMO) system are implemented. To validate the results obtained by the simulator, the ITU-T Rec. P.863 and the WB E-model are used. Experimental results show how different wireless network configurations impact on speech quality. Performance evaluation results demonstrated a high correlation between the proposed SQPM and ITU-T Rec. P.863 results, reaching an PCC and an RMSE of 0.9901 and 0.1492, respectively. Therefore, our proposal intends to be useful for wireless network planning tasks.