## 5 Conclusions and Future Work
### Future Work  
Several experiments and methods remained unexplored and were left for future work:  
* Our target in this research is a Speaker Identification task, a multiclass problem. However, the objective of the device to be built in Bindi is a Speaker Verification system, thus the next  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Set 1 Mean** & **Set 1 Std** & **Set 1 + 2 Mean** & **Set 1 + 2 Std** \\ \hline
1 & 89.71 & 0.56 & 78.55 & 0.60 \\
2 & 98.59 & 0.16 & 97.37 & 0.21 \\
3 & 98.48 & 0.23 & 97.21 & 0.26 \\
4 & 89.97 & 0.39 & 80.46 & 0.53 \\
5 & 99.93  
step would be to transform the system into a binary setting. These two approaches are not straightforwardly comparable but we believe that the problems and solutions can be translated to one another.
* Although stress seems to be an emotional condition that usually precedes more intense emotions, we aim to find or record a database that includes emotions in speech during an assault situation--such as panic, anxiety or fear--to work with.
* Finding techniques to strengthen the system by degrading audios as if they had been recorded in a real environment. Perhaps to simulate real world situations in which the recorded voice is not clean, we could add noise to the same database used and analyze its effect, either by sounds recorded at outdoors and indoors atmospheres or with white/pink noise.
* Further analyzing the differences between neutral and stressed speech to find new modifications to be applied to neutral speech to transform it into appropriate synthetically stressed speech.
* Implementing new methods for recording stressed speech in the training phase using Bindi, such as Stroop Effect games [45] in which the speaker should experiment stress, would be a way to count with originally stressed samples in the training stage.
* Heart Rate Variability (HRV) is known to have a strong relationship with stress. To deepen into this question and further investigating, we could try to find correlations between HRV and stress, adding this other biometric feature to the stress-robust SI system.
* With the use of data augmentation techniques we have collected a much larger database and we could therefore employ more powerful Deep Learning algorithms in the future, provided the device employed is able to cope with it in real time.  
Methodology, software and experimentation, E.R.-G.; data curation, A.M.-S.; writing--original draft preparation; E.R.-G. and A.M.-S.. Conceptualization, methodology, supervision and writing--review and editing, A.G.-A. and C.P.-M.  
This work is partially supported by the Spanish Government-MinECo project TEC2017-84395-P and Madrid Regional Project Y2018/TCS-5046.  
The authors thank all of the members of UC3M4 Safety for their contribution and support in the definition of requirements and constraints of the present work to be included as a part of the Bindi devices. The authors also thank the committee of the IV Edition of the Pilar Azcarate Gender Violence Research Awards at University Carlos III Madrid, Spain for the prize awarded to the inception of this research that has provided high motivation.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results'.