# Abstract The rapid growth in demand for technical talent has created a competitive environment where candidates must excel not only in technical skills but also in managing stress and presenting themselves confidently during interviews. Traditional interview preparation methods focus primarily on coding skills, offering little support in managing the psychological aspects that often impact performance. MockMate addresses this gap by combining technical interview simulations with an AI-driven, real-time emotion detection system that analyzes facial expressions to provide feedback on emotional states, such as stress, confidence, and engagement, during practice interviews. The platform leverages machine learning models for emotion detection using facial recognition techniques, allowing users to simulate technical interviews while receiving feedback on both their technical answers and emotional responses. A user-friendly interface, powered by React.js, offers an interactive coding environment, and a backend built with Node.js and Flask processes responses and emotions, storing progress data in MongoDB. TensorFlow.js enables real-time emotion analysis by capturing and interpreting facial expressions via the webcam, giving candidates instant insights into their emotional resilience during the interview process. MockMate empowers users to improve their technical and emotional preparedness, providing an analytics dashboard that tracks progress and highlights areas for improvement. Testing and user feedback suggest that MockMate enhances candidates’ confidence by combining technical rigor with emotional readiness, making it a valuable tool for comprehensive interview preparation. This project demonstrates the potential of AI in transforming interview preparation, offering insights for future work in expanding emotional intelligence capabilities in skill-building platforms. # Introduction 1.1 Problem Statement The increasing demand for skilled professionals in the technology sector has made interview preparation more challenging. Traditional methods of preparation, such as studying textbooks or practicing alone, often fail to simulate the stress and dynamics of a real interview environment. This project addresses the need for an interactive, AI-powered platform that helps candidates prepare for interviews, not only by improving their technical skills but also by enhancing their emotional intelligence and interview performance through emotion detection. 1.2 Objectives • Develop a web-based platform to provide mock interviews. • Implement AI-based emotion detection to evaluate user reactions. • Provide detailed feedback on performance, including technical answers and emotional responses. • Track user progress over time with analytics on key metrics such as problem-solving ability, emotional engagement, and stress levels. 1.3 Scope and Limitations The platform will be designed to handle mock interviews for technical roles, focusing on computer science concepts. Emotion detection will be limited to recognizing basic emotions such as stress, confidence, and engagement, using facial expression analysis. Limitations include potential variations in detection accuracy based on lighting conditions and user interactions. 1.4 Organization of the Report This report is structured to provide a clear and comprehensive overview of the project. It begins with an introduction to the problem at hand, followed by a detailed review of related literature. The report then moves on to the system design, where we explain the architecture and components of the platform. The methodology section outlines the approach used to develop and implement the system. After that, we discuss the implementation process, followed by the presentation of results. Finally, the conclusion summarizes the findings and suggests directions for future work. Additionally, the appendices include the code listings and a set of sample interview questions used during the experiments. # Related work Interview preparation tools like Leetcode, Hackerrank, and Pramp have become go-to platforms for candidates looking to sharpen their technical skills. These platforms provide coding problems, mock interviews, and problem-solving exercises, offering valuable practice for technical interviews. However, they often overlook the human side of the process, such as building confidence, managing stress, and improving body language—factors that are just as important as solving problems correctly. While many platforms focus on coding challenges and simulated interviews, they rarely provide real-time emotional feedback. This is a missed opportunity, as emotions play a critical role in interviews, influencing how candidates present themselves and connect with interviewers. Recently, there has been growing interest in incorporating emotion detection into interview preparation, acknowledging its importance in helping candidates become more self-aware and better prepared. The use of AI for emotion detection has already made waves in industries like healthcare and customer service, where understanding emotions is essential for improving interactions. In the context of interview training, AI can analyze facial expressions to offer insights into stress levels, engagement, and confidence. This helps candidates identify areas where they can improve, such as appearing more relaxed or engaged during mock interviews. However, creating an interactive interview platform that combines technical preparation with emotional feedback is no small task. Challenges include ensuring that emotion detection is accurate, providing feedback in real time without disrupting the experience, and designing a system that feels intuitive and user-friendly. Striking the right balance between advanced AI models and ease of use is critical to making such a platform truly effective and accessible to users. # Methodology Our approach focuses on creating a holistic platform that combines technical practice with emotional and interpersonal skill development, ensuring users are fully prepared for all aspects of real-world interviews. 1. Simulating a Real Interview Environment We’ve designed the platform to replicate the high-pressure atmosphere of real interviews. Timed sessions push users to solve problems within strict limits, just like in an actual interview. The platform isn’t just about technical challenges—it also tracks how users react under pressure, analyzing their emotional state as they tackle questions. This gives users a realistic experience and helps them build resilience. 2. Real-Time Emotional Feedback Interviews are as much about your emotional presence as they are about your answers. By using TensorFlow.js, we enable the platform to analyze facial expressions in real time through the user’s webcam. It detects key emotional cues like stress, confidence, and engagement and provides instant feedback during the session. This real-time guidance helps users recognize their emotions and adjust their behavior on the fly, making them more composed and confident. 3. Smart Emotion Recognition Models To ensure accuracy, the emotion recognition system is trained on a wide variety of facial expressions collected from diverse demographics. This ensures the platform works effectively for everyone, regardless of their background. We also use transfer learning to fine-tune the model, making it fast and reliable even on everyday devices. The result? A system that feels intuitive and trustworthy. 4. Holistic Performance Insights Success in interviews isn’t just about solving problems; it’s about how you present yourself. That’s why our platform combines technical metrics—like how accurately and quickly you answer coding questions—with emotional feedback, such as how confident and calm you appear. By addressing both technical and emotional aspects, the platform helps users become well-rounded candidates ready to face any challenge. 5. One-on-One Virtual Mock Interviews A key feature of our platform is the ability to participate in live mock interviews with a virtual interviewer. This feature goes beyond coding exercises, focusing on communication skills, tone, and overall presence. Users can simulate various interview rounds, like technical or HR interviews, and receive personalized feedback. This helps improve their clarity, professionalism, and ability to work effectively in a team—a crucial trait for most jobs. This methodology ensures users are not only technically sound but also emotionally prepared and polished communicators. By combining cutting-edge AI technology with practical, real-world scenarios, the platform empowers users to walk into interviews with confidence and leave a lasting impression. # Experiments This section outlines the tests conducted to evaluate how well our mock interview platform works in improving both technical skills and emotional readiness for interviews. The aim was to see if real-time emotional feedback and accurate emotion detection could help users prepare holistically for interviews. 1. User Testing and Feedback We had students and professionals take part in mock interviews with both coding and behavioral questions. During the session, the platform tracked their emotional state and provided real-time feedback. Afterward, participants shared their thoughts on how helpful the feedback was in managing stress and improving confidence. The results showed that emotional feedback positively impacted users' ability to stay calm and focused. 2. Emotion Detection Accuracy Participants were asked to express different emotions (like stress or confidence) while taking mock interviews. We compared the platform's emotion detection with their self-reports and evaluator observations. The system accurately identified emotions like stress and confidence, helping users better understand their emotional state during interviews. 3. Impact on Technical Performance We tested whether emotional feedback affected users' technical performance. Participants completed coding challenges, both with and without feedback. Those who received emotional feedback performed better—solving problems faster and more accurately—showing that emotional awareness helped them focus and reduce stress during tasks. 4. Improvement in Communication Skills We also tested how well the platform improved users' communication skills. Participants underwent mock technical and HR interviews where their tone, clarity, and confidence were evaluated. Feedback helped them improve their communication, making them more articulate and confident during interviews. 5. Long-Term Effectiveness To test if regular use led to lasting improvement, participants used the platform over several weeks. Tracking their progress, we found that they improved in managing emotions, solving problems more quickly, and communicating more effectively. 6. HR Professional Testing Finally, HR professionals from companies like Vividian Technology, Lenovo, and others tested the platform at our college’s HR summit. They evaluated how realistic and helpful the platform was for simulating real interview scenarios. They praised the emotional feedback feature, saying it could really help candidates prepare for high-pressure interviews. Their insights helped us refine the platform further. These experiments showed that the platform not only helps improve technical performance but also builds emotional readiness and communication skills, making it a comprehensive tool for interview preparation. # Results The results of our experiments provided valuable insights into how our platform enhances both technical and emotional interview preparation. User Feedback: Participants appreciated the emotional feedback, noting that awareness of their emotional state—such as stress or confidence—helped them stay focused and manage reactions better. This led to stronger performance in both technical and behavioral aspects of interviews. Emotion Detection Accuracy: The platform’s emotion detection system was effective, accurately identifying emotions like stress and confidence through facial expressions. Comparing the system's readings with participants’ self-reports and HR feedback validated its reliability in monitoring emotional states during interviews. Improved Technical Performance: Participants who received emotional feedback completed coding challenges faster and more accurately. The feedback helped them stay calm, improving their problem-solving efficiency under pressure. Better Communication Skills: Feedback on tone, clarity, and confidence helped users enhance their communication, especially in HR interviews, where these skills are critical. Users reported feeling more prepared for real-world interview scenarios. Long-Term Benefits: Over time, participants improved in managing stress, solving problems more effectively, and communicating confidently. Regular use of the platform showed consistent performance improvements, suggesting its value for long-term interview readiness. HR Professional Feedback: HR professionals from companies like Vividian Technology and Lenovo tested the platform during our college HR summit. They praised the emotional feedback feature, highlighting its potential to build emotional resilience for real-world interviews. They also noted the attention span tracker on our meeting platform, which helps assess user focus and engagement during mock interviews. Overall, the platform demonstrated significant benefits in preparing users both technically and emotionally, making it a comprehensive tool for interview success. # Discussion Our experiments have provided meaningful insights into how the platform can positively impact both the technical and emotional aspects of interview preparation. One of the most significant findings was how the emotional feedback influenced users' ability to manage their stress and stay focused during mock interviews. Participants consistently reported that being aware of their emotional state—whether feeling stressed, confident, or engaged—helped them control their reactions. This awareness allowed them to stay calm under pressure, which is crucial during actual interviews. The emotional feedback, in combination with the technical practice, provided a more holistic approach to interview prep. The accuracy of the emotion detection system also stood out. We found that the platform was effective at recognizing and categorizing emotions like stress, anxiety, and confidence by analyzing facial expressions. This was validated through self-reports from participants and feedback from human evaluators. The fact that the system could accurately assess emotional states in real time made it a valuable tool for interview preparation. By detecting emotions such as stress, the platform enabled users to adjust their behavior and mindset, improving their overall performance. From a technical standpoint, the emotional feedback improved participants' problem-solving abilities. Those who received feedback during coding challenges were able to stay calmer and solve problems more quickly and accurately. The platform allowed them to recognize when they were becoming stressed and take steps to calm themselves, leading to better results. It’s clear that managing one’s emotions during interviews has a direct impact on technical performance. The communication skills aspect also proved to be a valuable feature. During mock HR interviews, users appreciated the feedback on tone, clarity, and confidence. By receiving real-time suggestions on how to improve these areas, participants felt more prepared for actual HR interviews. Clear communication is often as important as technical knowledge, especially when it comes to making a good impression during the non-technical rounds. Another key takeaway was the long-term benefits of using the platform. Over time, users showed noticeable improvement in managing stress and communicating confidently. The results suggest that regular use of the platform can lead to continuous improvement, helping users build resilience and become more proficient in interviews. Finally, the feedback from HR professionals from Vividian Technology and Lenovo further validated the platform’s usefulness. They praised the emotional feedback feature, recognizing its potential to help candidates develop the emotional resilience needed for real interviews. They also pointed out the attention span tracker in the meeting platform, which allowed them to assess how engaged users were during mock interviews, providing another layer of insight into the preparation process. In conclusion, the results of our experiments demonstrate that the platform effectively supports both technical preparation and emotional readiness for interviews. The combination of coding practice, emotional feedback, and communication skill development makes this platform a unique and comprehensive tool for anyone looking to improve their interview performance. The feedback from both users and HR professionals reinforces the idea that a well-rounded approach to interview preparation can make a real difference in helping candidates succeed. # Conclusion To sum it up, our platform has proven to be a real game-changer for interview preparation. By combining technical skill training with emotional intelligence, we’ve created a more well-rounded approach that helps users excel in interviews. The real-time emotional feedback and emotion detection have been key in helping users manage stress, build confidence, and improve their performance. This unique combination of skills and emotional resilience means that users are not just preparing for coding challenges, but are also ready for the critical self-presentation moments during HR interviews. Users have shared how much they appreciated the emotional awareness, with many saying that simply knowing how they were feeling allowed them to stay calm under pressure and perform better. Additionally, the focus on communication—especially tone and clarity—helped users feel more confident when engaging in interviews, particularly during HR rounds. We’re especially proud of the feedback from HR professionals at companies like Vividian Technology and Lenovo. They recognized the value of the emotional feedback feature and the attention span tracker, highlighting the platform’s ability to simulate real interview scenarios and help candidates build emotional resilience. In conclusion, this platform isn’t just about teaching technical skills—it’s about empowering users to understand their emotions, communicate effectively, and approach interviews with newfound confidence. As users continue to use the platform, they can expect long-term benefits that go beyond just landing a job, setting them up for success in their careers. # References Speech emotion recognition using machine learning – A systematic review by Samaneh Madanian, Talen Chen, Olayinka Adeleye, John Michael Templeton, Christian Poellabauer , Dave Parry, Sandra L. Schneide Source : https://www.sciencedirect.com/science/article/pii/S2667305323000911 • Facial emotion recognition using deep learning : review and insights by Wafa Mellouk , Wahida Handouzi Source : https://www.sciencedirect.com/science/article/pii/S1877050920318019 • FACIAL EMOTION DETECTION AND RECOGNITION by Amit Pandey, Aman Gupta, Radhey Shyam Computer Science Department SRMCEM, AKTU Lucknow, India Source : https://www.researchgate.net/publication/361108119_FACIAL_EMOTION_DETECTION_AND_RECOGNITION • Development of an AI-Based Interview System for Remote Hiring by Byoung Chol Lee, Bo-Young Kim Source : https://www.researchgate.net/publication/357753685_Development_of_an_AIBased_Interview_System_for_Remote_Hiring - Our Custom Dataset Developed on Eleven Labs with Customized Voice In addition to the resources mentioned, our system also utilizes a custom dataset developed on Eleven Labs, featuring voice data that’s specifically tailored to enhance emotion detection accuracy. This dataset allows for a deeper understanding of how emotional tones manifest in speech, improving the overall performance and precision of our emotion recognition models. # Acknowledgement I would like to take a moment to express my sincere gratitude to everyone who helped make this project a reality. First and foremost, I’m incredibly thankful to Professor Nidhi Chitalia for her research, which was a tremendous help in shaping this project. Her work provided valuable insights that guided me through the development process, and I am truly grateful for her contribution. A special thank you to Dr. Sayantan Sinha, who has been an immense help from the beginning. His expert advice, guidance, and willingness to assist at every step made all the difference, and I am truly grateful for his support. I’m also grateful to the HR professionals from Vividian Technology and Lenovo for taking the time to test our platform and provide feedback during the HR summit. Their perspectives on the emotional feedback feature helped shape the project in meaningful ways. A big thank you to the authors of the papers I referenced, especially Samaneh Madanian, Wafa Mellouk, and others, whose research laid the foundation for this project. Their work was a key influence in the development of our system. I’d also like to acknowledge Eleven Labs for providing the resources and tools that helped us create a customized voice dataset, a key component that greatly improved the system’s performance. Finally, I want to express my gratitude to the participants who volunteered their time for our experiments. Their honest feedback and participation were essential in refining the platform. This project wouldn’t have been possible without the support and contributions of all those involved. I’m truly thankful to each and every one of you. # Appendix A. Dataset Overview For this project, we used a combination of facial expression datasets and customized voice samples created through Eleven Labs. The dataset covers a wide range of emotions such as stress, confidence, happiness, and anxiety, captured through both facial expressions and voice tone analysis. This diverse dataset allowed us to train our emotion detection system to recognize and respond to various emotional cues during interviews. B. Experiment Setup Hardware: CPU: Intel Core i7-9700K RAM: 16GB DDR4 Webcam: Logitech HD Pro Webcam C920 Microphone: Blue Yeti USB Microphone Software: Programming Language: Python 3.11 Libraries: TensorFlow, OpenCV, NumPy, Pandas, Flask Development Tools: Visual Studio Code Model Training: The emotion detection model was built using TensorFlow. We used transfer learning to fine-tune a pre-trained deep learning model with our trained dataset from ElevenLabs to focus on detecting emotions relevant to interview situations, such as stress and confidence. C. User Interface Design The platform’s interface is designed to be simple and easy to use. It allows users to navigate seamlessly between mock technical interviews and HR rounds. After each session, emotional feedback is displayed clearly, showing how users’ facial expressions and vocal tones changed during the interview. This feedback is available in a separate dashboard for easy review. D. Sample Feedback Display During each mock interview, users receive real-time feedback on the following: Emotional State: A graphical representation showing how the user’s emotions changed throughout the interview. Tone and Confidence: Feedback on voice clarity and confidence levels, helping users improve their communication skills. Stress and Engagement: Real-time insights into the user’s stress and engagement levels, especially during the coding and HR interview portions. E. HR Professional Feedback During testing, HR professionals from Vividian Technology and Lenovo provided valuable feedback: Vividian Technology: They found the emotional feedback feature to be a game-changer, noting its potential to help candidates build emotional resilience for real-world interviews. Lenovo: They praised the platform for simulating actual interview conditions, emphasizing how emotional feedback can make candidates feel more prepared and confident. F. Future Enhancements Looking ahead, there are a few key areas we plan to focus on: Voice Emotion Recognition: We aim to enhance our system by integrating deeper voice emotion recognition to complement the facial expression analysis. Expanded Dataset: The dataset will be expanded to cover more emotions and interview scenarios, improving accuracy. Advanced Analytics: We are working on adding more detailed real-time analytics to track behavior trends over multiple interview sessions, which will offer even more personalized feedback to users. This appendix provides additional technical details and insights into how the project was developed and tested.