BACKGROUND Background

 

 

• Self-Organizing Map
  • The SOM algorithm
  • About the ordering and convergence
    • Ordering of the SOM units.
    • Asymptotic density of the SOM units.
  • Some SOM applications and properties
• Learning Vector Quantization
  • LVQ algorithms
    • LVQ2.
    • LVQ3.
  • Minimization of classification errors
    • Measuring the misclassification.
    • Why LVQ?
• Hidden Markov Models
  • Description of the model
    • Assumptions and definitions.
    • Probability of the sequences.
    • Output density models.
    • HMM/ANN hybrids.
  • Limitations and gains
• Overview of speech recognition applications
  • ASR performance.
  • State-of-art in ASR error rates.
  • Examples of existing applications.