1. Pattern Recognition Applications Alexandros Potamianos Dept of ECE, Tech. Univ. of Crete Fall 2004-2005

2. Applications  Automatic Speech Recognition  Optical Character Recognition

3. Automatic Speech Recognition  Features: Mel Frequency Cepstrum Coefficients (MFCCs)  Model: Hidden Markov Model Each phone is a 3-state HMM  Parameter Estimation: EM (Baum-Welsh)  Recognition (classification in time): Viterbi

4. Common tricks  De-correlate features: MFCCs  Overcome observation independence assumption: take derivatives of features in time

5. Optical Character Recognition  Features: Pixel values or Wavelet transformations of pixel values  Model: Multi-layered Neural Network  Training: Back-propagation (Gradient descent)  Recognition (classification): discriminant function maximization (Bayes rule)