## 6 Conclusions  
This paper proposes the MiniatureVQNet, a single-ended speech quality evaluation method for VoIP audio applications, based on a lightweight deep neural network (DNN) model trained on an environmental noise and network-distorted speech dataset. The proposed model can predict audio quality independent of the source of degradation, whether noise or network, and is light enough to run in embedded systems. The proposed MiniatureVQNet model outperforms the traditional P.563 method in accuracy on all tested network conditions and environmental noise parameters. Furthermore, the proposed model is compact and can easily run on various low-resource computing platforms. Training on a noise-network distorted speech dataset improves the model prediction accuracy in all VoIP environment distortions compared to training the model on a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Correlation** & **MSE** \\ \hline
**MiniatureVQNet-Noise-Network** & 0.691 & 0.194 \\ \hline
**Float16 MiniatureVQNet–Noise–Network** & 0.690 & 0.194 \\ \hline
**Full-Integer MiniatureVQNet–Noise–Network** & 0.670 & 0.254 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation and Mean Squared Error. Figure 8: Effect of packet loss on prediction accuracy. noise-only dataset. The noise-network dataset captures speech degradation from environmental noise and also transmission-induced degradations. In all considered environmental noise and transmission error factors, the MiniatureVQNet trained on the noise-network-degradated dataset outperformed the model trained on the noise-only dataset. Hence, the MiniatureVQNet model can learn new features without a degradation in performance. Examining the model performance on different noise types, SNR, jitter, packet loss, and delay provides the model's weak points, where the model performance is low. Care should be taken when deploying single-ended speech quality prediction models as the stated performance may not be applicable in all conditions. Recently, music streaming, BGM streaming, Internet radios, and IP audio applications are very popular. Thus, it is important to consider not only single-ended speech quality evaluation but also music quality evaluation. Music and speech are always mixed, such as in Internet radios, where there is usually no clear point when music or speech only will be broadcast. Hence, a general audio quality evaluation is important. Although only speech was considered in this study, a future study plan is to extend this to evaluate the general audio quality of IP audio applications, including both speech and music signals. Furthermore, we plan to use highly versatile codecs such as Opus [36], which scales from low-bitrate narrowband speech to high-fidelity full-band speech and supports packet loss concealment and the jitter buffer algorithm. Conceptualization, E.J.K. and Y.N. ; methodology, E.J.K. and Y.N. ; software, E.J.K. ; validation, E.J.K. ; formal analysis, E.J.K. ; investigation, E.J.K. ; resources, Y.N. and E.J.K. ; data curation, E.J.K. ; writing--original draft preparation, E.J.K  
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