Speaker Labels in STT Output: Formats, Configuration, and Accuracy for Multi-Speaker Audio
Blog post from Deepgram
Speaker labels in Speech-to-Text (STT) systems are crucial for transforming raw transcriptions into functional multi-speaker outputs, especially when they are accurate and easy to process. The article delves into the nuances of speaker diarization, which assigns speech segments to individual speakers, contrasting how different APIs handle these assignments and the implications on production. It highlights the importance of provider-specific schemas and configuration parameters in determining the reliability of multi-speaker transcripts, emphasizing the role of model version pinning and the choice between multichannel separation and diarization. The text also discusses the measurement and improvement of speaker label accuracy using Diarization Error Rate (DER) and confidence scores, suggesting that upstream audio quality and configuration choices significantly impact label performance. For effective multi-speaker transcription, the article recommends starting with the audio source and leveraging deterministic channel separation over model-based methods whenever feasible, while also incorporating confidence-based QA processes to preemptively identify misattributions.
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