# Frame-Based Phone Classification Using EMG Signals +
## 4 Discussion and Conclusions
These results suggest that developing an EMG-based SSI with sufficient performance for real-world applications requires a large and diverse database. While using a larger set of training data may potentially slow down the experiments and require additional resources, we firmly believe that it is crucial to leverage as much training data as possible, provided that sufficient processing capabilities are available and the addition of new data leads to improved model performance. Our rationale stems from the fact that an SSI system suitable for real-world applications requires extensive preparation to handle unseen data. The speaker-independent classification results demonstrate a substantial decrease in model accuracy when trained with data from other speakers, even when the amount of training data is comparable to the speaker-dependent, session-independent models. This suggests that the differences between speakers' data are substantial, making it challenging for the model to generalize to a different speaker. These differences can be attributed to various factors, such as differences in speakers' physiognomy, articulation manner, or speaking pace. These findings suggest that using an SSI trained on a different speaker presents extra difficulty. Further experiments are needed to investigate whether training the model with a more extensive database from a single speaker or with data from multiple speakers can enhance speaker-independent performance. It is important to note that during four sessions, we observed a deviation in the signals of one channel, which cast uncertainty on its quality. The classification accuracy of these sessions is indeed lower compared to the other sessions by the same speaker. The most likely cause for these signal deviations is the detachment of electrodes in this channel during recording or the use of defective electrodes or cables. Acquiring EMG data is a sensitive technique and can result in variations in EMG signals depending on the speaker and recording conditions. Considering all of our findings, we plan to record more data from fewer speakers for future studies to address the issue of inter-session variability. We believe that this strategy will allow us to collect a more diverse range of data and enhance the performance of the EMG-based SSI. Furthermore, we intend to undertake more complex tasks, such as direct speech generation from EMG signals, to achieve our ultimate goal of developing an EMG-based SSI for Spanish-speaking laryngectomees. Conceptualization, E.N. and I.H. ; methodology, I.S., E.d.B., I.H. and E.N. ; software, I.S., E.d.B. and X.d.Z. ; validation, I.S. and E.d.B. ; formal analysis, I.S. and E.d.B. ; investigation, I.S. and E.d.B. ; resources, I.H. and E.N. ; data curation, E.d.B., I.S. and X.d.Z. ; writing--original draft preparation, I.S. and E.d.B. ; writing--review and editing, I.S., E.d.B., I.H., E.N. and X.d.Z. ; visualization, I.S. ; supervision, I.H. and E.N. ; project administration, I.H. and E.N. ; funding acquisition, I.H. and E.N. All authors have read and agreed to the published version of the manuscript. This research was funded by Agencia Estatal de Investigacion grant number ref.PID2019-108040 RB-C21/AEI/10.13039/501100011033. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee CEISH of the UPV/EHU (project code M10_2021_269, act 142/2021, approved on the 23 rd of September 2021). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The ReSSInt-EMG database will be made publicly available once collection of the recordings is finished. **Acknowledgments:** We would like to thank all the participants for helping us build the database, knowing that without them, this study would not have been possible. **Conflicts of Interest:** The authors declare no conflict of interest.