# Frame-Based Phone Classification Using EMG Signals +
## 4 Discussion and Conclusions  
This paper presents the results of phone classification experiments conducted on the new ReSSInt-EMG database. Compared to previous work [34], we revisited the linear discriminant analysis (LDA) reduction procedure, which resulted in changing the number of LDA features from 28 to 21. The change in the number of features used to train the model helped to reduce the training time and the complexity of the model, but the obtained accuracy remained similar. Furthermore, we included new sessions from the speakers that were already part of the database and recordings from two new speakers and extended the experiments with different modalities regarding speaker and session dependency. To accommodate the increased complexity of these experiments, we also used a neural network as a classification method instead of a bagging classifier. The session-dependent classification results show varying outcomes not only across speakers but also across multiple sessions from the same speaker. Furthermore, the session-independent results indicate a substantial decrease in testing accuracy when the model is applied to data from sessions not included in the training phase, with the magnitude of this effect differing between the two speakers. The decrease in testing accuracy observed when training with data from a session different from the one used to test the model is likely due to inter-session variability, which can be attributed to several factors. First, despite the use of a 3D mask, variations in electrode placement can occur between sessions. Second, the physical or mental state of the speaker may lead to slight differences in articulation between sessions, as each is recorded on a different day. For instance, a person may articulate differently when feeling exhausted, resulting in less articulation effort. Third, environmental conditions such as temperature and humidity can affect the speaker's state and the contact between the electrodes and the skin. High temperatures may cause increased sweating and decreased motivation. These \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training** & **Training** & **Testing Session** & **Testing Speaker** &
\begin{tabular}{c} **Testing** \\ **Accuracy** \\ \end{tabular} \\ \hline \multirow{6}{*}{001} & \multirow{6}{*}{102,103,104} & \multirow{6}{*}{101} & 002 & 19.47 \\  & & & 003 & 14.47 \\  & & & 004 & 12.08 \\  & & & 005 & 9.33 \\  & & & 006 & 8.41 \\ \cline{2-5}  & \multirow{6}{*}{101,103,104} & \multirow{6}{*}{102} & 002 & 18.28 \\  & & & 003 & 15.10 \\  & & & 004 & 6.91 \\  & & & 005 & 19.90 \\  & & & 006 & 8.51 \\ \cline{2-5}  & \multirow{6}{*}{101,102,103} & \multirow{6}{*}{104} & 002 & 8.36 \\ \cline{2-5}  & & & 002 & 10.47 \\ \hline \multirow{6}{*}{002} & \multirow{6}{*}{102,103,104} & \multirow{6}{*}{101} & 001 & 14.07 \\  & & & 003 & 15.71 \\ \cline{1-1}  & & & 004 & 14.78 \\ \cline{1-1}  & & & 005 & 8.11 \\ \cline{1-1}  & & & 006 & 10.53 \\ \hline \multirow{6}{*}{002} & \multirow{6}{*}{101,103,104} & \multirow{6}{*}{102} & 001 & 15.95 \\  & & & 003 & 18.09 \\ \cline{1-1}  & & & 004 & 10.26 \\ \cline{1-1}  & & & 005 & 16.90 \\ \cline{1-1}  & & & 006 & 7.21 \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{101,102,104} & 103 & 001 & 16.79 \\ \cline{1-1} \cline{2-5}  & & & 001 & 20.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Speaker-independent, session-independent classification results. factors can impact the recorded EMG signals, resulting in each session being recorded under unique circumstances. Consequently, a model that can identify patterns in the EMG signals of one session may struggle to recognize those same patterns in signals from a different session. Interestingly, when additional session data are added to the training data, testing accuracy increases. Given a corresponding decrease in validation accuracy, we believe that the improvement is due to enhanced diversity and representation of the data, allowing the model to better generalize beyond the training data.