Hidden Markov Models: Difference between revisions
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''Claimed by Ömer and | ''Claimed by Ömer, Jocomin, and Ding.'' | ||
== Introduction == | == Introduction == | ||
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Many real-world applications present hidden variables that are only observable through some emitted outcome, e.g. a speech signal of a word is observed rather than the specific phoneme states that are the underlying hidden states. To determine what the sequence of phonemes (states) would be that results in that specific word, the model learns the relation between the observed and unobservable variables.<ref>Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. ''Technometrics'', ''33''(3), 251-272.</ref> | Many real-world applications present hidden variables that are only observable through some emitted outcome, e.g. a speech signal of a word is observed rather than the specific phoneme states that are the underlying hidden states. To determine what the sequence of phonemes (states) would be that results in that specific word, the model learns the relation between the observed and unobservable variables.<ref>Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. ''Technometrics'', ''33''(3), 251-272.</ref> | ||
The technique behind Hidden Markov Models has been shown to be related to [[Dynamic Time Warping]].<ref>Juang, B. H. (1984). On the hidden Markov model and dynamic time warping for speech recognition—A unified view. ''AT&T Bell Laboratories Technical Journal'', ''63''(7), 1213-1243.</ref><ref>Fang, C. (2009). From dynamic time warping (DTW) to hidden markov model (HMM). ''University of Cincinnati'', ''3'', 19.</ref> | |||
== Historical Context == | == Historical Context == | ||
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== Key Innovations == | == Key Innovations == | ||
Some key innovations in the field of [[Speech Recognition]] using Hidden Markov Models include: | |||
* [[Dragon Dictate]] | |||
* [[DARPA Speech Understanding Research]] | |||
* Voice assistants, such as [[Introduction of Voice Assistants|Siri]]: before the [[Deep Learning Revolution]] and the [[Development of End-to-End Models]], | |||
== Impact on the Field == | == Impact on the Field == | ||
Revision as of 12:06, 16 September 2023
Claimed by Ömer, Jocomin, and Ding.
Introduction
A Hidden Markov Model (HMM) is a temporal probabilistic model in which some "hidden" or unobservable states are described by observable variables that are generated by these hidden states. [1] These hidden states adhere to the Markov property, meaning that the future state is only dependent on the current state. Since one cannot observe the underlying states of a specific model, learning the transition function of this sequence of states involves aligning the HMM to the observable states.[2][3]
Many real-world applications present hidden variables that are only observable through some emitted outcome, e.g. a speech signal of a word is observed rather than the specific phoneme states that are the underlying hidden states. To determine what the sequence of phonemes (states) would be that results in that specific word, the model learns the relation between the observed and unobservable variables.[4]
The technique behind Hidden Markov Models has been shown to be related to Dynamic Time Warping.[5][6]
Historical Context
Key Innovations
Some key innovations in the field of Speech Recognition using Hidden Markov Models include:
- Dragon Dictate
- DARPA Speech Understanding Research
- Voice assistants, such as Siri: before the Deep Learning Revolution and the Development of End-to-End Models,
Impact on the Field
Future Research
LLM Review
References
Here thus are the references: [7]
- ↑ Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc..
- ↑ Eddy, S. R. (1996). Hidden markov models. Current opinion in structural biology, 6(3), 361-365.
- ↑ Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.
- ↑ Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. Technometrics, 33(3), 251-272.
- ↑ Juang, B. H. (1984). On the hidden Markov model and dynamic time warping for speech recognition—A unified view. AT&T Bell Laboratories Technical Journal, 63(7), 1213-1243.
- ↑ Fang, C. (2009). From dynamic time warping (DTW) to hidden markov model (HMM). University of Cincinnati, 3, 19.
- ↑ Placeholder Reference