I've been diving into the gap between benchmark ASR performance and real-world speech. Models like Whisper and Deepgram show impressive >95% accuracy in ideal conditions. But in the wild — accents, noisy environments, emotional speech, code-switching, overlapping speakers — accuracy often drops sharply, often to the mid-80s or worse.
This matters because the next wave of AI won't be chatbots; it will be hands-free, real-time systems in contexts like:
- care work (voice logs) - crisis communication - home healthcare - security rounds - field operations - "I need help" micro-interactions
In these high-stakes contexts, 85% accuracy means critical information gets lost.
What seems missing today:
- Fine-tuning pipelines for noisy, accented speech - Reinforcement learning loops (user corrections → model improvements) - Fast per-speaker adaptation - Better handling of disfluencies ("uh," "um," repairs) - Scaling-law insights applied to ASR models - Evaluation metrics that reflect real environments instead of curated datasets
What I'm trying to understand:
- What prevents ASR from reaching reliable >99% accuracy in real-world conditions? - Is the bottleneck the model architecture, data quality, or something else?
Would love to hear from anyone who has:
- Worked on Whisper fine-tuning - Tackled multilingual or accented ASR - Shipped speech systems in noisy environments - Developed conversational (not dictation) ASR models - Built correction-feedback training loops - Deployed ASR in safety-critical or field environments
What worked? What failed? What surprised you?