Editing Hidden Markov Models (section)

== Key Innovations ==

Key innovations in the domain of [[Speech Recognition|speech recognition]] harnessing Hidden Markov Models, include: <ref>Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. ''Technometrics'', ''33''(3), 251-272.</ref>

* The DRAGON System, initially introduced by Dr. James Baker, marked a significant milestone in the evolution of speech recognition systems, and later evolved into [[Dragon Dictate]].<ref>Baker, J. (1975). The DRAGON system--An overview. ''IEEE Transactions on Acoustics, speech, and signal Processing'', ''23''(1), 24-29.</ref> DRAGON pioneered the use of Hidden Markov Models (HMMs) by employing a probabilistic framework that encapsulated the knowledge from training data within transition matrices and conditional probability matrices, establishing a foundation for robust and speaker-agnostic recognition. This innovative approach expedited the identification of optimal recognition paths through dynamic programming. Subsequently, [[Carnegie Mellon's Harpy System]] further refined these principles by incorporating speech-dependent heuristics and implementing various enhancements to significantly improve system performance and accuracy.<ref>Lowerre, B. T. (1976). The Harpy speech recognition system [Ph. D. Thesis].</ref>
* Within the realm of speech recognition research, [[DARPA Speech Understanding Research]] initiatives played a crucial role by funding multiple laboratories engaged in advancing the field. Projects such as BYBLOS and SPHINX prominently utilized Hidden Markov Models (HMMs) as a core component of their speech recognition systems, contributing to the development of more sophisticated and effective speech processing technologies.<ref>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.</ref><ref>Lee, K. F. (1988). ''Automatic speech recognition: the development of the SPHINX system'' (Vol. 62). Springer Science & Business Media.</ref>
* Furthermore, before the era of the [[Deep Learning Revolution]] and the emergence of [[Development of End-to-End Models|end-to-end models]], many [[Introduction of Voice Assistants|voice assistants]] systems, including the well-known Siri, relied on HMMs as a fundamental part of their speech recognition pipelines. HMMs played a crucial role in enabling these early voice assistants to understand and interpret spoken language, marking a significant chapter in the history of speech recognition technology.<ref>Domingos, P. (2015). ''The master algorithm: How the quest for the ultimate learning machine will remake our world''. Basic Books.</ref>
In summary, Hidden Markov Models have exerted a profound impact on the field of speech recognition, offering expedited, simplified, and versatile alternatives to conventional knowledge representation models.