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== Historical Context ==
== Historical Context ==


The origin of Hidden Markov Models (HMM) dates back to 1907, where Andrei Markov formulated Markov chains, which had proved that dependent variables were also affected by the law of large numbers, rather than only independent variables.<ref>Gagniuc, P.A. (2017). Historical Notes. In Markov Chains, P.A. Gagniuc (Ed.). https://doi.org/10.1002/9781119387596.ch1</ref> Although this process was known for several decades, it was not until the 1960’s that Leonard Baum and Ted Petrie began to create a new model that would achieve the most likely estimate of the parameters of the Markov chain, further refining the probability equation to now find the hidden paths to the process.<ref>Baum, Leonard E., and Ted Petrie. “Statistical Inference for Probabilistic Functions of Finite State Markov Chains.” The Annals of Mathematical Statistics 37, no. 6 (December 1966): 1554–63. https://doi.org/10.1214/aoms/1177699147.</ref><ref>Nilsson, Mikael, and Marcus Ejnarsson. “Speech Recognition Using Hidden Markov Model,” n.d.</ref> Yet, it was Jelinek, Bahl, and Mercer who first utilized the Markov model in speech recognition, in their attempt to move away from speaker dependent probabilities, and has become one of the most common uses of HMM.<ref>Jelinek, F., L. Bahl, and R. Mercer. “Design of a Linguistic Statistical Decoder for the Recognition of Continuous Speech.” IEEE Transactions on Information Theory 21, no. 3 (May 1975): 250–56. https://doi.org/10.1109/TIT.1975.1055384.</ref><ref>Stamp, Mark. “A Revealing Introduction to Hidden Markov Models.” In Introduction to Machine Learning with Applications in Information Security, by Mark Stamp, 7–35, 1st ed. Chapman and Hall/CRC, 2017. https://doi.org/10.1201/9781315213262-2.</ref>
The origin of Hidden Markov Models (HMM) dates back to 1907, where Andrei Markov formulated Markov chains, which had proved that dependent variables were also affected by the law of large numbers, rather than only independent variables, and was heavily influenced by the Bernoulli model.<ref>Gagniuc, P.A. (2017). Historical Notes. In Markov Chains, P.A. Gagniuc (Ed.). https://doi.org/10.1002/9781119387596.ch1</ref> Although this process was known for several decades, it was not until the 1960’s that Leonard Baum and Ted Petrie began to create a new model that would achieve the most likely estimate of the parameters of the Markov chain, further refining the probability equation to now find the hidden paths to the process.<ref>Baum, Leonard E., and Ted Petrie. “Statistical Inference for Probabilistic Functions of Finite State Markov Chains.” The Annals of Mathematical Statistics 37, no. 6 (December 1966): 1554–63. https://doi.org/10.1214/aoms/1177699147.</ref><ref>Nilsson, Mikael, and Marcus Ejnarsson. “Speech Recognition Using Hidden Markov Model,” n.d.</ref> Yet, it was Jelinek, Bahl, and Mercer who first utilized the Markov model in speech recognition, in their attempt to move away from speaker dependent probabilities, and has become one of the most common uses of HMM.<ref>Jelinek, F., L. Bahl, and R. Mercer. “Design of a Linguistic Statistical Decoder for the Recognition of Continuous Speech.” IEEE Transactions on Information Theory 21, no. 3 (May 1975): 250–56. https://doi.org/10.1109/TIT.1975.1055384.</ref><ref>Stamp, Mark. “A Revealing Introduction to Hidden Markov Models.” In Introduction to Machine Learning with Applications in Information Security, by Mark Stamp, 7–35, 1st ed. Chapman and Hall/CRC, 2017. https://doi.org/10.1201/9781315213262-2.</ref>


Further amendments and improvements to combat frequent issues with the model have been made since, most notably in the 1980's and 1990's<ref>Gales, Mark, and Steve Young. “The Application of Hidden Markov Models in Speech Recognition.” Foundations and Trends® in Signal Processing 1, no. 3 (February 20, 2008): 195–304. https://doi.org/10.1561/2000000004.</ref>, including shared-distribution HMM, which more easily dealt with huge numbers of parameters with limited training data<ref>Hwang, Mei-Yuh, and Xuedong Huang. “Shared-Distribution Hidden Markov Models for Speech Recognition.” IEEE Transactions on Speech and Audio Processing 1, no. 4 (October 1993): 414–20. https://doi.org/10.1109/89.242487.</ref>, Hierarchical Hidden Markov Models, which generalized standard HMMs and made the hidden states autonomous models, leading to sequences rather than single symbols being output.<ref>Fine, Shai, Yoram Singer, and Naftali Tishby. “The Hierarchical Hidden Markov Model: Analysis and Applications.” Machine Learning 32, no. 1 (July 1, 1998): 41–62. https://doi.org/10.1023/A:1007469218079.</ref>, and signal decomposition, where parallel HMMs are used to simultaneously recognise concurrent events, e.g. separating background noise from speech.<ref>Varga, A.P., and R.K. Moore. “Hidden Markov Model Decomposition of Speech and Noise.” In International Conference on Acoustics, Speech, and Signal Processing, 845–48. Albuquerque, NM, USA: IEEE, 1990. https://doi.org/10.1109/ICASSP.1990.115970.</ref>
Further amendments and improvements to combat frequent issues with the model have been made since, most notably in the 1980's and 1990's<ref>Gales, Mark, and Steve Young. “The Application of Hidden Markov Models in Speech Recognition.” Foundations and Trends® in Signal Processing 1, no. 3 (February 20, 2008): 195–304. https://doi.org/10.1561/2000000004.</ref>, including shared-distribution HMM, which more easily dealt with huge numbers of parameters with limited training data<ref>Hwang, Mei-Yuh, and Xuedong Huang. “Shared-Distribution Hidden Markov Models for Speech Recognition.” IEEE Transactions on Speech and Audio Processing 1, no. 4 (October 1993): 414–20. https://doi.org/10.1109/89.242487.</ref>, Hierarchical Hidden Markov Models, which generalized standard HMMs and made the hidden states autonomous models, leading to sequences rather than single symbols being output.<ref>Fine, Shai, Yoram Singer, and Naftali Tishby. “The Hierarchical Hidden Markov Model: Analysis and Applications.” Machine Learning 32, no. 1 (July 1, 1998): 41–62. https://doi.org/10.1023/A:1007469218079.</ref>, and signal decomposition, where parallel HMMs are used to simultaneously recognise concurrent events, e.g. separating background noise from speech.<ref>Varga, A.P., and R.K. Moore. “Hidden Markov Model Decomposition of Speech and Noise.” In International Conference on Acoustics, Speech, and Signal Processing, 845–48. Albuquerque, NM, USA: IEEE, 1990. https://doi.org/10.1109/ICASSP.1990.115970.</ref>

Revision as of 09:52, 17 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

The origin of Hidden Markov Models (HMM) dates back to 1907, where Andrei Markov formulated Markov chains, which had proved that dependent variables were also affected by the law of large numbers, rather than only independent variables, and was heavily influenced by the Bernoulli model.[7] Although this process was known for several decades, it was not until the 1960’s that Leonard Baum and Ted Petrie began to create a new model that would achieve the most likely estimate of the parameters of the Markov chain, further refining the probability equation to now find the hidden paths to the process.[8][9] Yet, it was Jelinek, Bahl, and Mercer who first utilized the Markov model in speech recognition, in their attempt to move away from speaker dependent probabilities, and has become one of the most common uses of HMM.[10][11]

Further amendments and improvements to combat frequent issues with the model have been made since, most notably in the 1980's and 1990's[12], including shared-distribution HMM, which more easily dealt with huge numbers of parameters with limited training data[13], Hierarchical Hidden Markov Models, which generalized standard HMMs and made the hidden states autonomous models, leading to sequences rather than single symbols being output.[14], and signal decomposition, where parallel HMMs are used to simultaneously recognise concurrent events, e.g. separating background noise from speech.[15]

In later years, namely the latter half of the 1980’s, HMM was being used for DNA sequencing and biological computations.[16]

Key Innovations

Some key innovations in the field of speech recognition using Hidden Markov Models include [17]:

  • The DRAGON System developed by Dr. James Baker was one of the earlier speech recognition systems that used HMMs and later became known as Dragon Dictate.[18] DRAGON is a probabilistic model that represents all knowledge from the training set of utterances in a transition matrix and a matrix of conditional probabilities between the hidden states and the observable states. This allowed the system to be speaker-agnostic, while being quick in finding the optimal path of recognition through dynamic programming. Carnegie Mellon's Harpy System improved upon DRAGON by incorporating speech-dependent heuristics and other improvements to increase the performance. [19]
  • DARPA Speech Understanding Research funded multiple laboratories in speech recognition, including BYBLOS and SPHINX, both using HMMs. [20][21]
  • Many voice assistants used HMMs before the Deep Learning Revolution and the development of end-to-end models, an example is Siri. [22]

All in all, the impact of HMMs in speech recognition has been significant as faster, more simplified, and more generalized alternatives to conventional knowledge representation models.

Impact on the Field

Future Research

LLM Review

References

Here thus are the references: [23]

  1. ↑ Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc..
  2. ↑ Eddy, S. R. (1996). Hidden markov models. Current opinion in structural biology, 6(3), 361-365.
  3. ↑ Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.
  4. ↑ Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. Technometrics, 33(3), 251-272.
  5. ↑ 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.
  6. ↑ Fang, C. (2009). From dynamic time warping (DTW) to hidden markov model (HMM). University of Cincinnati, 3, 19.
  7. ↑ Gagniuc, P.A. (2017). Historical Notes. In Markov Chains, P.A. Gagniuc (Ed.). https://doi.org/10.1002/9781119387596.ch1
  8. ↑ Baum, Leonard E., and Ted Petrie. “Statistical Inference for Probabilistic Functions of Finite State Markov Chains.” The Annals of Mathematical Statistics 37, no. 6 (December 1966): 1554–63. https://doi.org/10.1214/aoms/1177699147.
  9. ↑ Nilsson, Mikael, and Marcus Ejnarsson. “Speech Recognition Using Hidden Markov Model,” n.d.
  10. ↑ Jelinek, F., L. Bahl, and R. Mercer. “Design of a Linguistic Statistical Decoder for the Recognition of Continuous Speech.” IEEE Transactions on Information Theory 21, no. 3 (May 1975): 250–56. https://doi.org/10.1109/TIT.1975.1055384.
  11. ↑ Stamp, Mark. “A Revealing Introduction to Hidden Markov Models.” In Introduction to Machine Learning with Applications in Information Security, by Mark Stamp, 7–35, 1st ed. Chapman and Hall/CRC, 2017. https://doi.org/10.1201/9781315213262-2.
  12. ↑ Gales, Mark, and Steve Young. “The Application of Hidden Markov Models in Speech Recognition.” Foundations and Trends® in Signal Processing 1, no. 3 (February 20, 2008): 195–304. https://doi.org/10.1561/2000000004.
  13. ↑ Hwang, Mei-Yuh, and Xuedong Huang. “Shared-Distribution Hidden Markov Models for Speech Recognition.” IEEE Transactions on Speech and Audio Processing 1, no. 4 (October 1993): 414–20. https://doi.org/10.1109/89.242487.
  14. ↑ Fine, Shai, Yoram Singer, and Naftali Tishby. “The Hierarchical Hidden Markov Model: Analysis and Applications.” Machine Learning 32, no. 1 (July 1, 1998): 41–62. https://doi.org/10.1023/A:1007469218079.
  15. ↑ Varga, A.P., and R.K. Moore. “Hidden Markov Model Decomposition of Speech and Noise.” In International Conference on Acoustics, Speech, and Signal Processing, 845–48. Albuquerque, NM, USA: IEEE, 1990. https://doi.org/10.1109/ICASSP.1990.115970.
  16. ↑ Eddy, Sean R. “What Is a Hidden Markov Model?” Nature Biotechnology 22, no. 10 (October 2004): 1315–16. https://doi.org/10.1038/nbt1004-1315.
  17. ↑ Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. Technometrics, 33(3), 251-272.
  18. ↑ Baker, J. (1975). The DRAGON system--An overview. IEEE Transactions on Acoustics, speech, and signal Processing, 23(1), 24-29.
  19. ↑ Lowerre, B. T. (1976). The Harpy speech recognition system [Ph. D. Thesis].
  20. ↑ Chow, Y., Dunham, M., Kimball, O., Krasner, M., Kubala, G., Makhoul, J., ... & Schwartz, R. (1987, April). BYBLOS: The BBN continuous speech recognition system. In ICASSP'87. IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 12, pp. 89-92). IEEE.
  21. ↑ Lee, K. F. (1988). Automatic speech recognition: the development of the SPHINX system (Vol. 62). Springer Science & Business Media.
  22. ↑ Domingos, P. (2015). The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books.
  23. ↑ Placeholder Reference
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