AI-Interview Analyzer Using Natural Language Processing and Speech Recognition

The proposed AI Interview Analyzer provides an efficient approach for automated interview evaluation using Natural Language Processing and Speech Recognition techniques. The system mainly focuses on text-based interview analysis while also supporting speech-based interaction through speech-to-text  conversion.  By  using  keyword  matching,  TF-IDF vectorization, cosine similarity analysis, and automated feedback generation, the system helps candidates improve technical knowledge, communication skills, and interview performance. The study observed that many existing interview systems face challenges related to computational complexity, speech recognition errors, and privacy concerns. To address these issues, the proposed framework emphasizes lightweight NLP-based evaluation and scalable speech processing techniques. The system is mainly designed for IT-related interview domains and aims to provide an easy-to-use and effective interview preparation platform.   In future work, the system can be extended by incorporating multilingual interview support, adaptive AI-based question generation, emotion analysis, and advanced speech processing methods to further improve interview evaluation accuracy and user experience.

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