Speech-to-Text Accuracy Across Accents, Dialects, and Background Noise

The Reality of Telephony Audio

Most speech AI models are trained on pristine 44.1kHz studio recordings. But in the real world:

• Phone calls run over 8kHz G.711 narrow-band codecs.
• Callers are driving on highways with window noise, walking through crowded metro stations, or using cheap speakerphones.
• Global customers speak with rich regional accents and code-switch between languages (e.g. Hinglish).

Overcoming Acoustic Degradation

At Tunk.ai, our audio preprocessing pipeline includes:

1. Spectral Subtraction & Neural Denoising: We strip out stationary background hum (engines, air conditioning) and transient spikes (sirens, car horns) in the first 20ms of audio intake.
2. Multi-Model Dynamic Routing: Depending on the caller's country code, language, and initial audio characteristics, we dynamically route speech streams to the highest-performing regional model (e.g., Deepgram Nova-2, Sarvam AI, Whisper Large v3, or Google Cloud Speech).
3. Phonetic Keyword Boosting: Domain-specific terms like product names, account numbers, and industry acronyms are boosted in the acoustic decoder to virtually eliminate misspellings.

Conclusion

A voice agent is only as intelligent as what it can hear. By pairing robust noise filtration with accent-tuned transcription engines, Tunk.ai delivers voice interactions that feel effortless for every caller.