A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
Rabiner, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Proceedings of the IEEE)
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II.C The three basic problems for HMMs
- What are the three basic problems for HMMs?
The framing is due to Jack Ferguson (IDA).problem question solution 1 evaluation \(P(O \mid \lambda)\)? forward(–backward) procedure 2 decoding best state sequence \(Q\) for \(O\)? Viterbi algorithm 3 training adjust \(\lambda\) to maximize \(P(O \mid \lambda)\)? Baum–Welch (EM) - Rabiner, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Proceedings of the IEEE), II.C The three basic problems for HMMs
- How do the three problems combine in an isolated word recognizer?Train one HMM per vocabulary word on repetitions of it (Problem 3); segment training sequences into states to understand and refine the models (Problem 2); recognize an unknown word by scoring every word model and picking the highest likelihood (Problem 1).
- Rabiner, 1989, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (Proceedings of the IEEE), II.C The three basic problems for HMMs
Source
- Lawrence R. Rabiner. 1989. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. In Proceedings of the IEEE, pp. 257–286. doi:10.1109/5.18626