ASR (Automatic Speech Recognition)

Source: Dev.to

Overview

Yesterday I shared the full Voice AI pipeline.
Today we’re diving deep into Stage 1: ASR (Automatic Speech Recognition) – turning spoken words into text.

Feature Extraction

Raw audio → digital representation

• MFCCs (Mel‑Frequency Cepstral Coefficients)
• Spectrograms
• Filter Banks

Acoustic Modeling

Maps audio features to phonemes

• Traditional: HMM‑GMM, DNN‑HMM
• Modern: Transformers, Conformers

Decoding & Language Modeling

Phonemes → words using probabilities

• Beam Search
• CTC (Connectionist Temporal Classification)
• Attention mechanisms

Post‑Processing

Clean up the output

• Spell checking
• Punctuation
• Capitalization

Evolution of ASR

Traditional (1980s‑2010s)

• HMM + GMM
• Required phonetic alignment
• Separate components stitched together

State‑of‑the‑art (Now)

• Whisper: 680 K hours of training, 50+ languages
• Wav2Vec 2.0: Self‑supervised, works with limited data

Getting ASR wrong can cause the entire voice pipeline to fail; it’s the foundation of any Voice AI system.

What ASR model are you using? Any surprises with accuracy or latency?