Provider Comparison for Accent Handling

OpenAI Whisper

Accent strength: Excellent across 97+ languages Noise handling: Superior performance in challenging audio Dialect recognition: Best-in-class for non-standard accents Latency: 400-600ms (batch processing) Best for: Multilingual deployments, diverse accent populations

Why it excels: Trained on 680,000 hours of multilingual, multi-accent data including real-world audio conditions.

Deepgram

Accent strength: Good for standard American/British English Noise handling: Good with acoustic preprocessing Dialect recognition: Moderate for non-standard accents Latency: 200-300ms (streaming architecture) Best for: US-focused deployments prioritizing speed

Limitation: Training data skews toward standard English requiring custom models for accent diversity.

AssemblyAI

Accent strength: Very good across English variants Noise handling: Very good with advanced preprocessing Dialect recognition: Strong for English dialects, moderate for others Latency: 300-400ms Best for: English-primary deployments with some accent variation

Advantage: Custom vocabulary and phrase boost helps with accent-specific terminology.

Google Speech-to-Text

Accent strength: Good across 125+ languages Noise handling: Good with multi-channel audio Dialect recognition: Strong for major language variants Latency: 300-500ms Best for: Enterprise deployments with automatic language detection needs

Comparison Table

Provider	Languages	Accent Handling	Noise Robustness	Latency	Best Use Case
Whisper	97+	Excellent	Excellent	400-600ms	Diverse accents
Deepgram	36	Good	Good	200-300ms	Speed-critical
AssemblyAI	30+	Very Good	Very Good	300-400ms	English variants
Google STT	125+	Good	Good	300-500ms	Enterprise scale