Why it matters

ASR quality has jumped dramatically. Understanding options and constraints enables realistic voice apps.

Advertisement

The architecture

Neural end-to-end: audio → text tokens directly. No separate acoustic/language models.

Streaming vs offline: streaming for real-time UX; offline for accuracy.

Modern ASR pipelineAudio inputwaveform / featuresNeural modelencoder + decoderText outputwith timestampsStreaming ASR emits partial results as words identified; offline waits for full segment
ASR flow.
Advertisement

How it works end to end

Streaming: word-by-word output. Some quality trade-off vs offline.

Language coverage: models like Whisper cover 99+ languages. Smaller languages have less training data.

Domain adaptation: general models good; specialized domains (medical, legal) benefit from fine-tuning.

Accuracy metrics: Word Error Rate (WER). Commercial systems typically 5-15% WER on clean audio.