AI-Assisted Structured Interview Analysis Using Natural Language Processing and Speech Feature Extraction

Structured interviews are widely used in recruitment, psychological assessment, and social research due to their standardized procedures, fixed question sets, and unified evaluation criteria, which ensure a certain degree of fairness and reliability compared with unstructured interviews. However, traditional structured interview evaluation relies heavily on manual scoring by professional raters, which inevitably faces problems such as strong subjectivity, high time consumption, and inconsistent evaluation standards. Subjective biases, such as the halo effect, first-impression bias, and personal preference, often affect the objectivity of evaluation results; meanwhile, manual transcription of interview audio, coding of answers, and scoring of multiple dimensions are extremely time-consuming, making it difficult to meet the needs of large-scale interview scenarios. To solve these problems, this study proposes an AI-assisted framework for structured interview analysis that combines Natural Language Processing (NLP) and speech feature extraction. The proposed system can automatically complete the transcription of interview audio, extract linguistic features (including semantics, keywords, sentiment, and logical structure) from the transcribed text, and capture paracoustic features (including pitch, intensity, speech rate, and pause characteristics) from the audio signal. A multi-modal fusion model is constructed to integrate these text and speech features, thereby generating objective evaluation scores and competency assessments for interviewees. Experiments on a real structured interview dataset show that the proposed method not only improves the accuracy and consistency of interview evaluation but also significantly reduces the manual workload and weakens the impact of subjective bias. This research provides a reliable, efficient, and standardized tool for structured interview analysis, which can be widely applied in corporate recruitment, public institution selection, and educational assessment scenarios.