# ODIN112-AI-Assisted Emergency Services in Romania
## 6 Conclusions  
In our previous research [8], we have benchmarked various automatic speech recognition models and argued that our Kaldi is superior when it comes to its accuracy measured as WER. In this article, we have further improved our model and retrained with a far larger amount of diverse data. The accuracy levels proven by the speech technologies presented in this paper, including the automatic speech recognition one, make it a viable solution for building assistants that can help emergency services operators in making decisions. We observed an increase in WER when the models are trained on a larger amount of data because the vocabulary is more complex and there are more variations in speech signals. However, from our lab tests that simulate real-world emergency conversations, this translates to much better transcriptions. We believe the extended dataset provides the system with a better chance of learning abstract speech rather than some very specific words or voices. In the future, we look towards further expanding our training datasets by collecting more speech recordings through our crowdsourcing platform, Echo. Future work includes extending the neural network by increasing its parameter number in order to learn more from the training data. Finally, using richer language models and more elaborate preprocessing methods will further increase the value of the extracted features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Works**} & **EMO-IIT Original/GSM** & **Classifier Type** & **Speech Features** & **Test Set** & **Result** \\ \hline [PERSON] et al. [44] & original & VGG16 & Mel spectrograms & \
\begin{tabular}{c} 70/30 \\ random split \\ \end{tabular} & 71.00\% A \\ \hline [PERSON] et al. [46] & original & VGG16 &
\begin{tabular}{c} MFCC\&chroma\& \\ Mel spectrogram\& \\ contrast\&tonnetz \\ \end{tabular} & CV-5 folds & 86.10\% A \\ \hline
**[PERSON] et al. [45]** & **original** & **CNN-LSTM** & **Mel spectrograms** & **CV-5 folds** & **95.89\% A** \\ \hline [PERSON] et al. [29] & original & VGG16-MLP & \begin{tabular}{c} HP \& 80/10/10 \\ Mel spectrograms \\ \end{tabular} & \begin{tabular}{c} 80/10/10 \\ random split \\ \end{tabular} & 92.79\% A \\ \hline [PERSON] et al. [30] & \begin{tabular}{c} original \\ GSM \\ \end{tabular} & AlexNet & Mel spectrograms & CV-10 folds & \begin{tabular}{c} 80.50\% WA \\ 76.80\% WA \\ \end{tabular} \\ \hline Our model & \begin{tabular}{c} original \\ GSM \\ \end{tabular} & VGG16 & \
\begin{tabular}{c} log Spectrograms \\ \end{tabular} & CV-5 folds &
\begin{tabular}{c} 92.28\% WA \\ 83.86\% WA \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of EMO-DB results (bold marks the best results). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Works**} & **EMO-IIT Original/GSM** & **Classifier** & **Speech Features** & **Test Set** & **Result** \\ \hline [PERSON] and Zbancioc [24] & original & DL-CNN & Mel spectrograms\&MFCCs & CV-10 folds & 84.48\% A \\ \hline [PERSON] and Feraru [28] & original & DL-CNN & Mel spectrograms & CV-10 folds & 84.71\% A \\ \hline \multirow{2}{*}{**Our model**} & **original** & **VGG16** & **Log spectrograms** & **CV-5 folds** & \
\begin{tabular}{c} **90.24\% WA** \\ **91.20\% WA** \\ \end{tabular} \\ \hline \hline \end{tabular}
\begin{tabular}{c} CV—crossvalidation. WA—weighted accuracy. A—accuracy. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of EMO-IIT results (bold marks the best model). For emotional speech recognition, the best results in real-life applications are achieved with natural corpora. However, natural corpora are difficult to develop because recordings of actual emotional speech are not readily available and because, in some cases, the content can cause emotional harm to the listener. We are currently annotating such a corpus recorded from real-life emergency calls. Conceptualization, T.B., M.D., I.B. and F.P. ; methodology, M.D. and I.B. ; software, D.U., S.-A.T., I.-D.F., B.-C.M., I.A. and B.M.