Deception detection in text, speech and gestures using machine learning
مقدم أطروحة جامعية
مشرف أطروحة جامعية
أعضاء اللجنة
الجامعة
جامعة الأخوين
الكلية
كلية الهندسة و العلوم
القسم الأكاديمي
هندسة البرمجيات
دولة الجامعة
المغرب
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2017
الملخص الإنجليزي
In legal systems worldwide, court trials decisions are made through a process that is carried according to applicable laws in order to prove defendants’ guilt or innocence.
Testimonies of the defendants and the witnesses are relevant to the decision-making process, and evaluating the honesty of the testimonies is critical for making accurate decisions.
Automating deception detection in court hearings could significantly affect trial decisions and requires a reliable system that can effectively and accurately identify deceptive behavior.
The identification of deceptive behavior is a topic that has been researched in several fields, such as psychology and criminology, which provided valuable information for understanding the differences between deceptive and truthful behavior.
This work explores the applicability of machine learning for detecting deception in real-life trial data.
The data used consists of real-life trial data recorded in the US for which the corresponding transcriptions and annotation of gestures is provided.
The evaluation of machine learning techniques in classifying hearing testimonies as deceptive or truthful is performed using text, speech and gestures, which correspond to the testimonies transcriptions, audio recordings, and annotated gestures respectively.
As part of this work, a comprehensive toolkit for detecting deception in audiovisual recordings was developed and includes the functionalities needed for building machine learning models from text, speech, and gestures and evaluating them.
The system developed provides a basis for evaluating deception detection using different datasets and configurations, as well as performing deception detection on audiovisual recordings, without technical knowledge, using existing models.
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
عدد الصفحات
43
قائمة المحتويات
Table of contents.
Abstract.
Abstract in Arabic.
Abstract in French.
Chapter One : Introduction.
Chapter Two : Background.
Chapter Three : Literature review.
Chapter Four : System features and architecture.
Chapter Five : System implementation.
Chapter Six : Evaluation and results.
Chapter Seven : Conclusion.
References.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Utarid, Husam. (2017). Deception detection in text, speech and gestures using machine learning. (Master's theses Theses and Dissertations Master). Al Akhawayn University, Morocco
https://search.emarefa.net/detail/BIM-775463
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Utarid, Husam. Deception detection in text, speech and gestures using machine learning. (Master's theses Theses and Dissertations Master). Al Akhawayn University. (2017).
https://search.emarefa.net/detail/BIM-775463
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Utarid, Husam. (2017). Deception detection in text, speech and gestures using machine learning. (Master's theses Theses and Dissertations Master). Al Akhawayn University, Morocco
https://search.emarefa.net/detail/BIM-775463
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-775463
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
