# Meta Learning Based Deception Detection from Speech
## 5 Evaluation
### Results  
In this section, we describe our results for deception detection. We considered training the basic MNA model using 25, 50, and 100 epochs as well as batch sizes of 32, 128, 512, and 1000. Since using 50 epochs and a batch size of 512 performed best for the basic MNA model, we used these hyper-parameters for all the models that we have tested. In addition, we use weighted categorical cross-entropy loss to deal with the imbalance of our data. The results presented in this paper are the average of 30 different executions, each with a different seed. The scores are calculated on the query sets of the test tasks and can be seen in Table 5. We note that since the False samples are the minority, and those which we are trying to identify as being deceptive, we use them as the positive class, and the True samples are used as the negative class. Therefore, False samples classified as being False are considered true positives. Similarly, True samples classified as being False are considered false positives. As depicted by the Table, the CHAML-Wav2 Vec 2.0 model outperformed all other methods with an accuracy of 61.34% and an F1-Score of 0.3857. Figure 4 provides the confusion matrix for the CHAML model on the Wav2 Vec 2.0 embedding. Next, we compare CHAML to the MNA classifier. As shown in the table, CHAML outperformed the fine-tuning method and improved the results of the MNA classifier for both embedding types. We note that the FSFM original work [55] (which MNA's architecture was inspired by it) presented higher accuracy and F1-score results than in our experiments. This is due to the fact that in the original work, the entire data set was first shuffled and then split into training and test sets. Therefore, the same person also appeared in the training set and also in the test set. This allowed the model to learn from all types of people. However, in our current work, there is no overlapping between samples of the same person in both train and test sets. Consequently, the performance decreases as the model is trained on some people but tested on others. In addition, CHAML is compare to prototypical and MAML methods, which are commonly used for meta-learning. The Prototypical network was trained using 50 epochs for each support set and used weighted categorical cross-entropy loss. For obtaining the prototypical embedding for each class, we use the FSFM architecture without the last layer used for the classification task; the prototypical embedding length is 128. MAML was trained on 50 epochs with 15 epochs for each task's fine-tuning and used the FSFM model as the meta-learner. MAML performed better when using random weighted sampling rather than weighted categorical cross-entropy loss for accounting for the imbalance in the data. Clearly, since the Prototypical network and MAML are trained on each task separately, they must use a single support set for all the query samples, unlike CHAML, which has different support sets for each query sample in the training tasks. The results show that CHAML outperforms other meta-learning methods. The Prototypical network achieved the lowest accuracy compared to all other methods and a lower F1-score compared to CHAML, while MAML achieved similar results. The averaged computation times of the three models can be seen in Table 6. As shown, CHAML is over 25 times faster than MAML  
Figure 4: CHAML—Wav2 Vec 2.0 performance on the query set of the test samples.