Signal Processing and Speech Communication Laboratory
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Automatic Speech Recognition

Education level

General Information

This lecture course addresses the issue of automatic speech recognition, i.e. mapping an acoustic signal to a sequence of words. First various concepts of pattern recognition are introduced including hidden Markov models and language models built from large corpora of (symbolic) text. The course Speech Communication I is not required for this course.

The current lecture material can be found in the TeachCenter.


  • Introduction to Automatic speech recognition (ASR)
  • Speech Production & Phonetics
  • Feature extraction
  • Classification
  • Estimation of probability distributions
  • Gaussian Mixture Models
  • Markov models
  • Hidden Markov models (HMMs)
  • Grammar models - Language Models
  • Decoding (Viterbi decoder)
  • Deep Neural Networks for Speech Recognition

Lecture notes (old slides)


Speech recognition

  • D. Jurafsky et al: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Prentice-Hall 2009.
  • X. Huang, A. Acero, H.-W. Hon: Spoken Language Processing: A Guide to Theory, Algorithm and System Development. Prentice Hall PTR 2001.


  • R.O. Duda and P.E. Hart: Pattern Classification and Scene Analysis. Wiley and Sons, Inc., 1973.
  • C. M. Bishop: Pattern Recognition and Machine Learning, Springer, 2006