Developer builds scoring system to shortlist TTS voices, cutting selection time by 83%

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A developer working with a text-to-speech system that generates varied voices using captions and random seeds faced a time-consuming selection process, with 24 candidates per role requiring up to 30 minutes of listening per role. To streamline this, they identified the acoustic properties — speech rate, pitch range, volume, and stability — that distinguish voices suited for roles like narrator, counselor, and emcee. They built a Python-based scoring pipeline that measures these properties from short probe sentences and ranks candidates using role-specific weighted formulas, including a target speech-rate band rather than a simple directional preference. The system narrows 24 candidates down to the top 3 per gender per role, reducing per-role selection time from 30 minutes to roughly 5 minutes. Final voice selection is still made by human ear, with the author noting that the metrics serve as a filtering tool, not a decision-maker.

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