# Meta Learning Based Deception Detection from Speech
## 2 Related Work  
### Deception Detection  
The deception detection task has been explored in many types of research using different approaches and techniques: some based on text, some on speech, some on video, and others on physiological measures. In the beginning, most research was focused on analyzing physiological measures, such as breathing rate, heart rate, blood pressure, and body temperature [8; 9; 10]. Other studies found the connection between deception and human behaviors [11; 12; 13]. However, detecting the physiological measures of a human requires special instruments and may be invasive and expensive [14; 13]. Therefore, many studies researched the use of machine learning methods for deception detection. For detection based on text problems, [PERSON] et al. [4] develop a corpus of deceptive reviews and use Naive Bayes and Support Vector Machine (SVM) as the classifiers utilizing Linguistic Inquiry and Word Count (LIWC) combined with bigrams. [PERSON] et al. [15] show that using Context Free Grammar (CFG) parse trees consistently improves detection performance. [PERSON] et al. [16] use the BERT (Bidirectional Encoder Representations from Transformers) network and show that compared with truthful text, deceptive text tends to be more formulaic and less varied. There have been only a few works that attempt to detect deception based on speech cues. [PERSON] et al. [17] develop a corpus of deceptive speech using one on one interviews. [PERSON] et al. [18] use SVM model utilizing Mel Frequency Cepstral Coefficient(MFCC). The MFCC is a feature representation commonly used in speech processing and speech recognition tasks. MFCCs are derived from the spectral representation of a speech signal and are used to capture the spectral characteristics of the signal, such as the frequencies of the various speech sounds and the power spectrum of the signal. MFCCs are commonly used for speech analysis in various domains, such as voice recognition [19], speech recognition [20], emotion recognition [21], animal vocalizations [22], and neonatal bowel sound detection [23; 24]. [PERSON] et al. [25] train a classifier using combined both linguistic features and acoustic features as a combination of MFCC and prosodic features and [PERSON] et al. [26] use an LSTM neural network on a set of MFCC characteristics extracted from audio speech to deception detecting based on voice stress. [PERSON] et al. [27] extract variable length frame-level speech features from different length's speech samples and use a recurrent neural network combined with a convolution operation as their model. Other studies focus on deception detection in videos using different feature extraction methods such as IDT (Improved Dense Trajectory) feature, and high-level features represent facial micro-expressions extracted from the videos, with machine learning techniques [28; 29; 30; 31]. [PERSON] et al. [28] develop an automated deception detection model that consists of three main modules: face focused cross-stream network which deep joint feature learning from facial expressions and body motions for video, a meta-learning module, and an adversarial learning module that generates a 256-dimension feature vector for each synthesized video. The meta-learning module was used to deal with their data scarcity problem by using pairwise comparison. Each deceptive video sample was combined with four true samples to generate five pairs. The model outputs the probability for each pair to be from the same class. Other researchers apply a multi-modal approach for deception detection from video data sets. [PERSON] et al. [6] introduce a collected data-set consisting of videos collected from public court trials. They apply a multi-modal approach for deception detection on their data set using inputs from different modalities, i.e., video, audio, and text. [PERSON] et al. [32] use common machine learning techniques such as SVM, Naive Bayes, Decision Trees, Random Forests, Logistic Regression, and Adaboost.