## 2 Deep-Learning-Based Single-Ended Speech Quality Measures
### Training Datasets  
Parametric models evaluate the quality from a set of parameters without processing the actual audio signal samples. Parametric models are commonly used in VoIP applications and are derived from models such as the E-model [19]. Although the E-Model is the most extensively applied parametric objective assessment method, it was originally designed for conventional network planning. Parametric models evaluate the quality of experience (speech quality experienced by VoIP users) in VoIP networks based on network quality of service (QoS) mapping and machine learning algorithms [20; 21; 22; 23].  
Parametric model studies are primarily concerned with the effect of network conditions on the speech quality experienced by users. The network packet loss effect is the most studied. The method proposed in [24] evaluated the influence of packet loss on the Skype quality and proposed a simplified E-Model. In [25], the authors addressed the effect of burst packet losses on VoIP. In [26], the effect of different codec and network conditions on the speech quality was examined. In [22], work was carried out to improve the E-model for wireless communication systems based on wireless parameter values and consider current technologies, such as MIMO. Moreover, an approach that considered the interactivity of voice communications was developed by [PERSON] and [PERSON] [21], where the E-Model and PESQ were combined to evaluate the voice quality, and a nonlinear regression model of voice quality was proposed. Parametric models can evaluate distortions due to network conditions and devices used. However, as they do not analyze the actual audio samples, if the samples are distorted due to environmental noise, parametric models cannot be applicable for evaluating speech quality.  
Network distortions and environmental noise distortions affect speech quality. Speech signals carry the effect of both noise and network distortions. Models trained on noise and network-distorted speech signals can learn to predict the speech quality regardless of whether the speech is affected by noise or environmental noise or both. In [23], a model trained and evaluated on real PSTN call recordings from 80 providers in more than 50 countries was proposed. After going through a gateway, the signals were routed across one of five PSTN carrier networks that Skype and Microsoft Teams use. Background noise was artificially added in the call to model environmental noise. In [27], an open-source PSTN speech quality test model based on a dataset with over 1000 crowdsourced real phone calls was presented. The influence of file cropping on the perceived speech quality and the influence of the number of ratings and training size on the model accuracy were analyzed. The proposed models in [23; 27] were trained on a public network and crowdsourced data, and the distortion parameters such as packet loss and environmental noise were not controlled. Therefore, the speech quality distortions could not be independently analyzed, and the model accuracy in different conditions could not be verified. In this work, in addition to the public available dataset, we trained a model in a controlled dataset, where all factors are known and analyzed.