Evaluating speech-to-text models

In an in-depth evaluation of six speech-to-text (STT) models conducted across 240 audio cases and eight content domains, the study highlights the importance of selecting the right STT provider for voice agents by focusing on transcription accuracy and its impact on downstream responses. The evaluation used a comprehensive methodology that included assessing transcription similarity, critical entity recall, and answer equivalence, alongside real-time latency measurements. The study found that while all models were closely matched in accuracy, OpenAI's gpt-4o-transcribe emerged as the top choice, offering the best balance of answer quality and low latency. It emphasized the value of using domain-specific vocabularies and post-transcription corrections to enhance the performance of STT systems, particularly for structured tokens like IDs and callsigns, which prove challenging for many models. Additionally, the research underscored the need to incorporate audio review in the evaluation process to differentiate between genuine errors and reference issues, suggesting that the choice of model should align with project-specific priorities, whether it be accuracy, speed, or the preservation of critical information.

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