Automated Speech Recognition (ASR)

Definition

Automated Speech Recognition (ASR) is the technology that converts spoken language into text automatically using AI models. It powers transcription and voice-driven applications.

Purpose

The purpose is to allow machines to understand human speech. It is used in voice assistants, dictation tools, customer service, and accessibility technologies.

Importance

  • Core technology behind voice interfaces.
  • Helps break down barriers for people with disabilities.
  • Accuracy varies with language, accent, and background noise.
  • Requires continuous improvement with new data.

How It Works

  1. Capture audio input through a microphone or file.
  2. Process and normalize the audio signal.
  3. Extract features (e.g., phonemes, acoustic models).
  4. Apply language models to interpret speech contextually.
  5. Output text for further use.

Examples (Real World)

  • Apple Siri: ASR used in voice assistant.
  • Google Cloud Speech-to-Text API: transcription for apps.
  • Microsoft Azure Cognitive Services: ASR for enterprise applications.

References / Further Reading

  • Automatic Speech Recognition — NIST.
  • Speech Recognition — IEEE Signal Processing Society.
  • Speech and Language Processing — Jurafsky & Martin, Stanford.