# Meta Learning Based Deception Detection from Speech
## 6 Conclusions & Future Work  
In this study, we have proposed CHAML, a comparative hint approach meta-learning for deception detection based on speech. The method is based on the meta-learning approach in which a model is trained on various learning tasks to enable it to solve new learning tasks using only a few samples. In our approach, we added some labeled samples (the support set) to each unlabeled sample (the query set) from the same task to predict its label. Our setting differs from the typical meta-learning problem since our data comes from different environments (and different people), whereas in the classical meta-learning framework, data from one environment is divided into different tasks. In addition, in typical meta-learning, there is no overlap between the classes in the training tasks and those in the test task. However, in our approach, classes are the same in all tasks, but the task environment differentiates each one from the other. Therefore, typical meta-learning methods do not perform well in our task, while CHAML, our proposed method, manages to gather relevant information from the support sets and improves the model's performance.  
In future work we attempt to find a way to add attention to CHAML, so that it learns how to attend to the relevant parts of each example when making a prediction. We note that our method is not limited to deception detection and can also be applied to other environments, in which all tasks, both in training set and in testing set, include the same classes (or regression problems), but the samples in different tasks highly differ from each other. Examples of such environments include emotion recognition (with many different people; each person is a different task), handwriting recognition (with different people; each person is a different task), and age and sex determination of different animals (each animal is its task), and determining whether sensor data collected from different environments indicates that human intervention is required. In future work, we intend to test CHAML on some of these environments.  
Conceptualization, N.M. and A.A.; methodology, N.M. and A.A.; software, N.M.; validation, N.M. and A.A.; formal analysis, N.M. and A.A.; investigation, A.A.; data curation, N.M.; writing--original draft preparation, N.M.; writing--review and editing, N.M. and A.A.; visualization, N.M.; supervision, A.A.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Ministry of Science, Technology & Space, Israel and the Ministry of Science and Technology of Taiwan.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Avg. Time in sec.** \\ \hline MAML [51] & 1047.71 \\ Prototypical [50] & 27.22 \\ CHAML & 41.07 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CHAML vs. Meta-Learning Methods Average Computation Times.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Ariel University (AU-NAT-AA-20210816 16 August 2021).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** All data is available at: deception_data.zip.  
**Conflicts of Interest:** The authors declare no conflict of interest.