# Meta Learning Based Deception Detection from Speech
## 1 Introduction  
Lying and deception are an inherent part of human nature, while some lies are considered small and may even be helpful for a smoother interaction between humans, others may be devastating and cause major damage. However, despite deception detection being essential to everyone in their daily life, it is challenging for humans to determine whether a person is being deceptive [1, 2]. Therefore, throughout history, many methods and devices were developed for that task [3], and more recently, machine learning methods based on text, video, and speech [4, 5, 6]. Typically, works for speech deception detection do not separate train and test based on the person so that an individual may have examples in both train and test [5]. Nor do most works split the training and test data according to the recording environment. This results in less reliable models, as in practice, the model must learn about some population in some recording environment and then be used to produce predictions for a different population in a different recording environment. Consequently, in this work, we split the data according to the persons, i.e., some people are used for training, and others are used for testing only. One of the main difficulties in deception detection based on speech is to learn the features of lying from some people and in some recording environments and apply this knowledge to others, despite the fact that different people tend to lie differently. Therefore, in order to achieve high performance, the model is required to learn which speech-related features are specific to a person and which are general. In addition, we consider a meta-learning approach for deception detection based on speech. For that, we assume that we have very few labeled samples for each person (namely, two positive samples and two negative ones). This approach is inspired by thewell-known polygraph test [7], in which several comparison questions are asked at the beginning of the polygraph interview in order to obtain physiological measures of the subject when telling the truth and when lying. Namely, in our approach, the model is trained on a set of training tasks; each task represents a person from the subjects in our data set and consists of a support set used for learning about the task and a query set used to evaluate the performance of this task. The support set contains four examples from the person's samples, two positive samples and two negative ones, and the query set contains the remaining samples of the person. Our approach to meta-learning differs from the typical one. In a typical meta-learning problem, there are typically several classes in each task, which differ from task to task. In addition, training and test tasks typically have different classes. However, in our setting, we have the same two classes for all tasks, but each task represents a different person from our data, and therefore, the data features are very different between different tasks. We present an innovative method of deception detection in the meta-learning setting and show that it outperforms the existing state-of-the-art methods of deception detection based on speech and other approaches for meta-learning. In our method, we use the comparative hint approach, which gives the model hints about the new environment (in our case, a new person) by providing some true and false examples from the same person sample set together with the tested sample. Namely, our model processes the positive and negative pairs and combines the result of this process with the tested sample into a vector fed into a neural classifier. We believe that by using our unique architecture, the model can compare the tested sample to the given hints, learn the person's way of lying, and improve its detection performance.