Development of Spectral Speech Features for Deception Detection Using Neural Networks

Fernandes, Sinead V.; Ullah, Muhammad Sana

doi:10.1109/iemcon53756.2021.9623077

Cited by 3 publications

(5 citation statements)

References 23 publications

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“…We hope that the following sections will [38] Neural Network / Accuracy: 1.0 [2018] Deception detection using artificial neural network and support vector machine [39] SVM / Accuracy: 1.0 [2020] Automated Deception Detection of Males and Females from Non-Verbal Facial Micro-Gestures [40] Random Forest / Accuracy: 0.998 [2019] Face-Focused Cross-Stream Network for Deception Detection in Videos [41] Neural Network / Area Under the Curve: 0.9978 [2018] A Multi-View Learning Approach To Deception Detection [42] Multi-view Learning / Accuracy: 0.98 [2015] A comparison of features for automatic deception detection in synchronous computer-mediated communication [43] Decision Tree / Accuracy: 0.98 [2019] Robust Algorithm for Multimodal Deception Detection [44] Combined methods / Accuracy: 0.97 [2018] Lie Detector with The Analysis Of The Change Of Diameter Pupil and The Eye Movement Use Method Gabor Wavelet Transform and Decision Tree [45] Decision Tree / Precision: 0.97 [2021] LieNet: A Deep Convolution Neural Networks Framework for Detecting Deception [46] Neural Network / Accuracy: 0.967375 [2017] Deep Learning Driven Multimodal Fusion for Automated Deception Detection [47] Neural Network / Accuracy: 0.964 [2019] How smart your smartphone is in lie detection? [48] KNN / Precision: 0.95 [2012] The Voice and Eye Gaze Behavior of an Imposter: Automated Interviewing and Detection for Rapid Screening at the Border [49] Decision Tree / Accuracy: 0.9447 [2020] Building a Better Lie Detector with BERT: The Difference Between Truth and Lies [50] Neural Network / Accuracy: 0.936 [2021] Deception detection in text and its relation to the cultural dimension of individualism/collectivism [51] Logistic Regression / Recall: 0.93 [2018] Deception detection in videos [52] Logistic Regression / Area Under the Curve: 0.9221 [2021] Development of Spectral Speech Features for Deception Detection Using Neural Networks [53] Neural Network / Accuracy: 0.9167…”

Section: Discussionmentioning

confidence: 99%

“…It is also a non-Deep Learning method. The three studies [38,53,69] that exploited this kind of Neural Network presented accuracies ranging from 0.7916 to 0.8750, with a mean at 0.8333 ± 0.0417.…”

Section: Artificial Neuralmentioning

confidence: 99%

“…LSTM model performances were measured by accuracy in 5 [38,50,53,65,81] out the 9 studies. Those accuracies range from 0.7487 to 1.0, with a mean at 0.8886 ± 0.0965.…”

Section: Artificial Neuralmentioning

confidence: 99%

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Deception detection with machine learning: A systematic review and statistical analysis

et al. 2023

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Several studies applying Machine Learning to deception detection have been published in the last decade. A rich and complex set of settings, approaches, theories, and results is now available. Therefore, one may find it difficult to identify trends, successful paths, gaps, and opportunities for contribution. The present literature review aims to provide the state of research regarding deception detection with Machine Learning. We followed the PRISMA protocol and retrieved 648 articles from ACM Digital Library, IEEE Xplore, Scopus, and Web of Science. 540 of them were screened (108 were duplicates). A final corpus of 81 documents has been summarized as mind maps. Metadata was extracted and has been encoded as Python dictionaries to support a statistical analysis scripted in Python programming language, and available as a collection of Jupyter Lab Notebooks in a GitHub repository. All are available as Jupyter Lab Notebooks. Neural Networks, Support Vector Machines, Random Forest, Decision Tree and K-nearest Neighbor are the five most explored techniques. The studies report a detection performance ranging from 51% to 100%, with 19 works reaching accuracy rate above 0.9. Monomodal, Bimodal, and Multimodal approaches were exploited and achieved various accuracy levels for detection. Bimodal and Multimodal approaches have become a trend over Monomodal ones, although there are high-performance examples of the latter. Studies that exploit language and linguistic features, 75% are dedicated to English. The findings include observations of the following: language and culture, emotional features, psychological traits, cognitive load, facial cues, complexity, performance, and Machine Learning topics. We also present a dataset benchmark. Main conclusions are that labeled datasets from real-life data are scarce. Also, there is still room for new approaches for deception detection with Machine Learning, especially if focused on languages and cultures other than English-based. Further research would greatly contribute by providing new labeled and multimodal datasets for deception detection, both for English and other languages.

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Section: Discussionmentioning

confidence: 99%

Section: Artificial Neuralmentioning

confidence: 99%

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Deception detection with machine learning: A systematic review and statistical analysis

et al. 2023

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“…The dataset is compared with the threshold values and that value is compared and predicts whether the person is in a stressed or unstressed condition (Fernandes & Ullah, 2021;Firoz et al, 2009). Some of the pre-processing steps are importing the dataset, importing the libraries and cleaning.…”

Section: Architecturementioning

confidence: 99%

Longitudinal Investigation of Work Stressors Using Human Voice Features

Natarajan¹,

Maheswaran²,

Dhanalakshmi³

et al. 2023

AIP

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Stress is a part of everyone's life. Any event or thought that makes you upset, furious or anxious can set it off. It will affect the human health mentally and physically and produce a negative impact on nervous and immune systems in our body. The human voice carries a lot of information about the person speaking. It also aids in determining a person's current state. In this proposed method, stress was detected using a deep learning model. Automatic stress detection is becoming an intriguing study topic as the necessity for communication between humans and intelligent systems rises. The hormone called cortisol can also be used to determine the body's stress state. For most people, however, it is not a viable option. Speech features are particularly affected by stress, which is combined with the aim that voice data would serve as an easy-to-capture measure of everyday human stress levels and hence as an early warning signal of stress-related health problems. The proposed technique extracts Mel filter bank spectral coefficients from pre-processed voice input and the spectrum coefficients are extracted. The features of Mel frequency cepstral coefficients are applied to feed-forward networks and long short-term memory to predict the status of stress output using a binary decision, i.e., unstressed or stressed. The Mel spectrum and spectrogram output shows the variation in stressed and unstressed voice features. The results of the proposed method indicate better performance compared to an existing model. The model was developed as a web application to be used by workers to test their state of stress at any time.

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“…For conduct this comprehensive research, the authors used the database which is a collection of utterances from the audio recording of a male suspect under criminal investigation. The suspect was determined to have given deceptive statements under questioning during polygraph testing [3], [4], [6], [12], [14], [86]. Audio recordings of three sessions of polygraph testing with the same questions by the investigator and the same responses by the suspect will be used for analysis and synthesis.…”

Section: A Experimental Databasementioning

confidence: 99%

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

Fernandes

Ullah

2022

IEEE Access

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Over a few decades, a remarkable amount of research has been conducted in the field of speech signal processing particularly on deception detection for security applications. In this study, a comprehensive review on recent machine learning approaches using verbal and non-verbal features is presented for deception detection. A brief overview on different feature extraction techniques, the results of recognition rate, and computational time based on machine learning methods are summarized in a tabular format. In addition, numerous datasets used as primary sources of deception detection in the review articles are also presented in this work. Key findings from the reviewed articles are summarized and a few major issues related to deception detection approaches are examined. A statistical analysis which conducted by extracting the significant information from the eighty-eight (88) scientific papers over the last thirty (30) years are provided in this review paper. The results emphasize on the trends of research in deception detection as well as further research opportunities for researchers as a part of continuous progress.

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Development of Spectral Speech Features for Deception Detection Using Neural Networks

Cited by 3 publications

References 23 publications

Deception detection with machine learning: A systematic review and statistical analysis

Deception detection with machine learning: A systematic review and statistical analysis

Longitudinal Investigation of Work Stressors Using Human Voice Features

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

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