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--- lecture_notes [2012/11/05 01:51] – hj
+++ lecture_notes [2012/11/19 16:00] (current) – hj
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   * {{:cse6328-f12-w6.pdf|Week 6}}: Hidden Markov Model (HMM): HMM vs. Markov chains; HMM concepts; Three algorithms (forward-backward, Viterbi decoding, Baum-Welch learning)  (Weekly Reading: {{:rabiner1_hmm_asr_part1.pdf|W6}}, the full version of this paper is not required but is {{:rabiner1_hmm_asr.pdf|here}} for your reference)
-  * {{:cse6328-f12-w8.pdf|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)
+  * {{:cse6328-f12-w8.pdf|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: {{{:htkbook31_part1.pdf|W8}}})
-  * Week 9: Automatic Speech Recognition (ASR) (II): Language Modelling (LM); N-gram models: smoothing, learning, perplexity, class-based.
+  * {{:cse6328_f12_w9.pdf|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)
+  * {{:cse6328_f12_w10.pdf|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)