How to build an AI-Powered interview scoring system with speech-to-text

An AI-powered interview scoring system effectively transforms interview assessments by recording interviews, converting speech to text, and systematically evaluating candidates based on structured criteria. By using Python and AssemblyAI's speech-to-text API, this system allows for the transcription of interviews with speaker separation, enabling an objective analysis of complete transcripts later. This method eliminates the need for simultaneous note-taking and evaluation during interviews, thus reducing cognitive overload and potential biases. It uses a 1-5 rating scale to score candidates' competencies, extracting evidence from transcripts to support evaluations with quotes and timestamps. The system provides advantages such as reduced bias, legal protection, and time savings, and it can be adapted for different roles by customizing scoring criteria and keywords. Furthermore, the system's effectiveness can be measured through metrics like time-to-hire and quality of hire, aiming for consistent and fair hiring decisions based on reliable, data-driven evidence.

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