Introduction to Speech Recognition

What ASR is, its evolution from HMMs to end-to-end deep learning, and key use cases.

Beginner · 12 min read

What is Speech Recognition?

Automatic Speech Recognition (ASR) converts spoken audio into text. It powers Siri, Google Assistant, live captions, medical dictation, and call center automation.

Era Technology Notes
1950s–1980s Template matching, DTW Digit recognition only, speaker-dependent
1990s–2000s Hidden Markov Models (HMM) Statistical, vocabulary-limited
2010s Deep Neural Networks + HMM Hybrid systems, major accuracy jump
2014+ Seq2Seq with CTC End-to-end, no HMM needed
2022+ Large self-supervised models Whisper, wav2vec 2.0 — near-human accuracy

Flow:

  1. Audio — Microphone input or WAV file
  2. Feature Extraction — MFCCs, mel spectrograms
  3. Acoustic Model — Maps audio to phonemes
  4. Language Model — Scores word sequence probability
  5. Transcript — Final text output

Part of the Speech Recognition & LLM Engineering series on Tekivex. Browse all tutorials or explore our open-source products.