# Meta Learning Based Deception Detection from Speech
## 4 Comparative Hint Approach Meta-Learning (CHAML)
### CHAMLCore  
In this model, we were inspired by the idea of meta-learning for few-shot learning for deception detection based on speech. We define each person in our data-set as a \"meta-learning task\". Each task's support set contains four randomly sampled examples, two per class, and the rest are the query set. In the training phase, each sample was trained with four examples randomly sampled from the training set of the person, i.e., each sample appears as a query, and may appear as an example in the support-set of other samples. Clearly, in the evaluation phase, we do not have different pairs of labeled examples for each query sample, and we use the same four examples for all query samples of a task. As mentioned in Table 2, there are 45 testing subjects (i.e., tasks), and for each of them, we have four labeled examples, two per class. Therefore, in total, we have 90 True and 90 False samples in the support set of the testing tasks. See sample partition details in Table 4.  
The CHMAL model fixes the order of the provided examples by first using the true examples and then the false examples. For each class' examples, we calculated its element-wise product; this is known to have the ability to catch similarities or discrepancies between the vectors. Each pair is then concatenated with the given product and fed into three fully connected layers with the ReLU activation function and dropout. The number of neurons in each fully connected layer depends on the original embedding size, which is a different value for each of the two different embedding types. That is, for the Five Sound Features embedding mentioned in Section 4.1.1 the embedding size is 193, and for the Wav2 Vec 2.0 embedding mentioned in Section 4.1.2 the embedding size is 1024. The first fully connected layer is three times the length of the embedding, which matches the size of its input (the embedding of the first example, the embedding of the second example. and the embedding of the dot product). The length of the second and final fully connected layers is twice the embedding size. The intuition behind this architecture is to allow CHAML to identify the patterns present in each class and return a general representation that contains the features that characterize each class. The results from both the positive and negative pairs are combined with the tested sample into a vector that is fed into a neural classifier. Finally, CHAML returns the probability that the tested sample belongs to each of the classes. In the training phase, the weights of the preprocess layers and the neural classifier are all updated after each epoch.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & **Support Set** & **Query Set** & **Total Samples** \\ \hline \multirow{2}{*}{**Train**} & **True** & 5288 & \multirow{2}{*}{ALL} & \multirow{2}{*}{ALL} & \multirow{2}{*}{7529} \\  & **False** & 2241 & & & \\ \hline \multirow{2}{*}{**Test**} & **True** & 2297 & 90 & 2207 & \multirow{2}{*}{3259} \\  & **False** & 962 & 90 & 872 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Support-Query Division.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & \\ \hline Sample frequency & 16 k Hz \\ Learning rate & \(1\times 10^{-4}\) \\ Training epochs & 5 \\ Training batch size & 12 \\ Gradient accumulation steps & 3 \\ Total train batch size & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyper-parameters used for fine-tuning the Wav2 Vec 2.0 model.  
We believe that the model compares the tested sample with the examples of each class in order to find which class is more similar to the tested sample, which helps it to provide a more accurate prediction. An illustration of CHAML's architecture is depicted in Figure 2.