Why speech-to-text accuracy matters upstream of your LLM

Speech-to-text accuracy is presented as a foundational determinant of downstream LLM reliability because language models treat transcripts as factual input and cannot reconstruct omitted or incorrectly transcribed information. The piece argues that aggregate word error rate is insufficient for evaluating risk, since mistakes involving names, numbers, negations, domain terminology, or speaker attribution can distort summaries, CRM records, retrieval results, compliance findings, and automated decisions far more severely than errors in filler words. It identifies noisy audio, accents, multilingual code-switching, specialized vocabulary, poor audio normalization, and diarization failures as common production challenges, and recommends testing providers on representative real-world recordings using entity-level, semantic-fidelity, and end-to-end extraction metrics rather than clean benchmark data alone. It also describes async transcription as generally more accurate than real-time streaming for post-call workflows, advises normalizing audio to mono 16 kHz and 16-bit depth, and notes that a two-pass LLM cleanup process can correct formatting and some surface errors but cannot restore missing or wrongly captured facts. Alongside these technical recommendations, the article promotes Gladia’s transcription models, managed API features, privacy options, benchmarking tools, and claims of lower operational burden than self-hosted systems.

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