AI BriefingElevenLabs Guide: Fixing Speech-to-Text Accuracy with Audio Quality and WER Measurement
·2026.11.04 21:00
Key point
The guide identifies audio input, speaker dynamics, and lexical issues as the primary causes of transcription errors and provides specific fixes for each.
Details
- Speech-to-text accuracy failures stem from three main sources: poor audio input quality, complex speaker dynamics, and lexical or contextual challenges like jargon and homophones.
- Optimize recording settings by using 16 kHz or 44.1 kHz sampling rates, lossless formats like FLAC or PCM WAV, and positioning microphones four to six inches from the speaker to preserve phonetic detail.
- Address overlapping speakers and specialized vocabulary using diarization, multichannel recording, and keyterm prompting to bias the model toward specific spellings and names.
- Evaluate performance using Word Error Rate (WER), calculated as (Substitutions + Deletions + Insertions) / Total Reference Words, while recognizing that WER weights all errors equally and may not reflect the impact of critical mistakes like drug names or account numbers.
- Distinguish between accuracy (matching ground truth) and precision (reliability of positive predictions), noting that a model can be precise but inaccurate if it deletes words consistently.
- Apply inverse text normalization (ITN) to standardize formatting, such as converting spoken numbers to written formats, and use human-in-the-loop review for high-stakes fields like medication doses.
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