lecture_notes
This is an old revision of the document!
- Week 1: background introduction; speech and spoken language (Weekly Reading: W1)
- Week 2: Math foundation: probabilities; Bayes theorem; statistics; Entropy; mutual information; decision tree; optimization (Weekly Reading: W2) (A useful online manual on Matrix Calculus)
- Week 4: Generative Models; model estimation; maximum likelihood, EM algorithm; multivariate Gaussian, Gaussian mixture model, Multinomial, Markov Chain model; (Weekly Reading: W4)
- Week 5: Discriminative Learning; Bayesian Learning; Pattern Verification
- Week 8: Automatic Speech Recognition (ASR) (I): ASR introduction; ASR as an example of pattern classification; Acoustic modeling: parameter tying (decision tree based state tying); (Weekly Reading: W8)
- Week 9: Automatic Speech Recognition (ASR) (II): Language Modelling (LM); N-gram models: smoothing, learning, perplexity, class-based.
- Week 10: Automatic Speech Recognition (ASR) (III): Search - why search; Search space in n-gram LM; Viterbi decoding in a large HMM; beam search; tree-based lexicon; dynamic decoding; static decoding; weighted finite state transducer (WFST) (Additional slides for WFST)
lecture_notes.1346352533.txt.gz · Last modified: 2012/08/30 18:48 by hj