Solutions for Accuracy Improvement

Fine-Tune with Accent-Specific Data

Custom model training: Providers like Deepgram enable custom model training using your actual call recordings Improvement: 10-20% accuracy gain for underrepresented accents Data requirements: Minimum 50-100 hours of transcribed audio from target accent population Process: Upload recordings, human-transcribe subset, provider trains custom model

Include Real Call Audio in Training

Studio recordings: Clean, perfect audio that doesn't match production Real call audio: Background noise, varied microphone quality, natural speech patterns Benefit: Models learn to handle imperfect conditions that actually occur

Best practice: Use last month's production calls (with consent) as next month's training data.

Acoustic Preprocessing

Noise suppression: Remove steady-state background noise (HVAC, fan noise) Echo cancellation: Eliminate acoustic reflections in large rooms Automatic gain control: Normalize volume levels across different microphone distances Dereverberation: Reduce room echo and reverb effects

Implementation: WebRTC provides these features built-in for browser-based voice. Telephony deployments benefit from provider-side enhancement.

Confidence-Based Clarification

STT confidence scoring: Models return confidence score (0-1) with each transcription Threshold setting: Configure agents to request clarification when confidence < 0.7 Clarification patterns: "Did you say you need to schedule an appointment?" confirms understanding User experience: Feels natural, doesn't require users to know system struggled

Custom Vocabulary and Phrase Boosting

Domain-specific terms: Add industry jargon, product names, proper nouns to model vocabulary Accent-specific spellings: Include common mispronunciations as valid alternatives Context hints: Provide likely words based on conversation context Benefit: 15-25% accuracy improvement for technical domains