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''Claimed by Ömer, Jocomin, and Ding.''
== Introduction ==
== Introduction ==


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The origins of HMMs trace back to 1907 when Andrei Markov introduced Markov chains, demonstrating that dependent variables are also subject to the law of large numbers, extending beyond the realm of independent variables.<ref>Gagniuc, P.A. (2017). Historical Notes. In Markov Chains, P.A. Gagniuc (Ed.). https://doi.org/10.1002/9781119387596.ch1</ref> However, it was not until the 1960s that Leonard Baum and Ted Petrie initiated the development of a novel model aimed at estimating the parameters of Markov chains with maximum likelihood. Their work refined the probability equations, enabling the inference of hidden paths within 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> Subsequently, Jelinek, Bahl, and Mercer pioneered the application of Markov models in speech recognition, particularly focusing on mitigating speaker-dependent probabilities. This marked a seminal moment in the widespread adoption of HMMs.<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 origins of HMMs trace back to 1907 when Andrei Markov introduced Markov chains, demonstrating that dependent variables are also subject to the law of large numbers, extending beyond the realm of independent variables.<ref>Gagniuc, P.A. (2017). Historical Notes. In Markov Chains, P.A. Gagniuc (Ed.). https://doi.org/10.1002/9781119387596.ch1</ref> However, it was not until the 1960s that Leonard Baum and Ted Petrie initiated the development of a novel model aimed at estimating the parameters of Markov chains with maximum likelihood. Their work refined the probability equations, enabling the inference of hidden paths within 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> Subsequently, Jelinek, Bahl, and Mercer pioneered the application of Markov models in speech recognition, particularly focusing on mitigating speaker-dependent probabilities. This marked a seminal moment in the widespread adoption of HMMs.<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>


Over the subsequent decades, substantial advancements and refinements have been made to address inherent challenges in HMMs. Notably, during the 1980s and 1990s<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>,  innovations such as shared-distribution HMMs which, instead of having each hidden state in the model associated with its distinct probability distribution, multiple hidden states share a common probability distribution. By doing so, SD-HMMs effectively reduce the number of model parameters, making them more manageable and robust, particularly when dealing 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 (HHMMs) represented a significant evolution from traditional HMMs by introducing a hierarchical structure that empowers individual hidden states to function autonomously and generate sequences of symbols or states, prompting advancements in natural language processing, and becoming crucial in speech recognition and machine translation.<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> Moreover, signal decomposition techniques involving parallel HMMs were developed to address concurrent event recognition challenges, enabling the simultaneous identification and separation of distinct events from complex acoustic signals. This innovation has proven particularly valuable in applications like audio processing, where isolating target sounds from background noise or identifying multiple overlapping events is essential for enhanced signal analysis and interpretation.<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>
Over the subsequent decades, substantial advancements and refinements have been made to address inherent challenges in HMMs. Notably, during the 1980s and 1990s<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>,  innovations such as shared-distribution HMMs in which, instead of having each hidden state in the model associated with its distinct probability distribution, multiple hidden states share a common probability distribution. By doing so, SD-HMMs effectively reduce the number of model parameters, making them more manageable and robust, particularly when dealing 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 (HHMMs) represented a significant evolution from traditional HMMs by introducing a hierarchical structure that empowers individual hidden states to function autonomously and generate sequences of symbols or states, prompting advancements in natural language processing, and becoming crucial in speech recognition and machine translation.<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> Moreover, signal decomposition techniques involving parallel HMMs were developed to address concurrent event recognition challenges, enabling the simultaneous identification and separation of distinct events from complex acoustic signals. This innovation has proven particularly valuable in applications like audio processing, where isolating target sounds from background noise or identifying multiple overlapping events is essential for enhanced signal analysis and interpretation.<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>


In subsequent years, notably during the latter part of the 1980s, HMMs began to play a pivotal role in the domains of DNA sequencing and computational biology. These mathematical models proved instrumental in decoding genetic information, facilitating the analysis of biological data, and unraveling the intricacies of genomic sequences, contributing significantly to the progress of molecular biology and bioinformatics during this period.<ref>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.</ref>
In subsequent years, notably during the latter part of the 1980s, HMMs began to play a pivotal role in the domains of DNA sequencing and computational biology. These mathematical models proved instrumental in decoding genetic information, facilitating the analysis of biological data, and unraveling the intricacies of genomic sequences, contributing significantly to the progress of molecular biology and bioinformatics during this period.<ref>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.</ref>
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== Key Innovations ==
== Key Innovations ==


Some key innovations in the field of [[Speech Recognition|speech recognition]] using Hidden Markov Models include <ref>Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. ''Technometrics'', ''33''(3), 251-272.</ref>:
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 developed by Dr. James Baker was one of the earlier speech recognition systems that used HMMs and later became known as [[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 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. <ref>Lowerre, B. T. (1976). The Harpy speech recognition system [Ph. D. Thesis].</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>
* [[DARPA Speech Understanding Research]] funded multiple laboratories in speech recognition, including BYBLOS and SPHINX, both using HMMs. <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>
* 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>
* Many [[Introduction of Voice Assistants|voice assistants]] used HMMs before the [[Deep Learning Revolution]] and the [[Development of End-to-End Models|development of end-to-end models]], an example is Siri. <ref>Domingos, P. (2015). ''The master algorithm: How the quest for the ultimate learning machine will remake our world''. Basic Books.</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>
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.
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.


== Impact on the Field ==
== Impact on the Field ==
Line 32: Line 30:
* In 1996, Bourlard and Dupont marked a significant milestone by employing HMMs in the domain of automatic subtitle generation. Their pioneering work showcased the versatility of HMMs in transcribing spoken content into textual subtitles. This breakthrough enhanced accessibility and facilitated the dissemination of audiovisual content, making it more inclusive and accessible to diverse audiences.<ref>H. Bourlard and S. Dupont, “A new ASR approach based on independent processing and recombination of partial frequency bands.” Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pages 426--429, Philadephia, Pennsylvania, October 1996.</ref>  
* In 1996, Bourlard and Dupont marked a significant milestone by employing HMMs in the domain of automatic subtitle generation. Their pioneering work showcased the versatility of HMMs in transcribing spoken content into textual subtitles. This breakthrough enhanced accessibility and facilitated the dissemination of audiovisual content, making it more inclusive and accessible to diverse audiences.<ref>H. Bourlard and S. Dupont, “A new ASR approach based on independent processing and recombination of partial frequency bands.” Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pages 426--429, Philadephia, Pennsylvania, October 1996.</ref>  
* In the domain of continuous speech recognition , these models demonstrate notable expertise in the representation of acoustic features inherent to speech and the formulation of language models conducive to the process of decoding spoken utterances. The incorporation of HMMs within continuous speech recognition systems has played a pivotal role in facilitating the attainment of robust and precise transcription capabilities. Consequently, this integration has substantially broadened the scope of applications within the field of voice recognition and transcription.<ref>Young S J, Evermann G, Gales M J F, et al. The HTK book version 3.4 Manual[J]. Cambridge University Engineering Department, Cambridge, UK, 2006.</ref>  
* In the domain of continuous speech recognition , these models demonstrate notable expertise in the representation of acoustic features inherent to speech and the formulation of language models conducive to the process of decoding spoken utterances. The incorporation of HMMs within continuous speech recognition systems has played a pivotal role in facilitating the attainment of robust and precise transcription capabilities. Consequently, this integration has substantially broadened the scope of applications within the field of voice recognition and transcription.<ref>Young S J, Evermann G, Gales M J F, et al. The HTK book version 3.4 Manual[J]. Cambridge University Engineering Department, Cambridge, UK, 2006.</ref>  
* While the landscape of speech recognition has evolved with the advent of deep learning and neural networks, HMMs have not faded into obsolescence. In 2021, MIT researchers showcased the enduring importance of HMMs by integrating them into a speaker-adapted HMM-GMM (Hidden Markov Model-Gaussian Mixture Model) Automatic Speech Recognition (ASR) system. This groundbreaking research involved training HMMs using audio data and source language VTT subtitles, leveraging the KALDI framework and Mel-Frequency Cepstral Coefficients (MFCC) features. The outcome was a model that seamlessly combined speech recognition and syntax translation, highlighting the adaptability and effectiveness of HMMs in addressing complex tasks at the intersection of speech processing and natural language understanding.<ref>Salesky E, Wiesner M, Bremerman J, et al. The multilingual tedx corpus for speech recognition and translation[J]. arXiv preprint arXiv:2102.01757, 2021.</ref> (Honestly, I don't fully understand all the technicalities of the "25th literature", but HMM played an important role in their research.)
* While the landscape of speech recognition has evolved with the advent of deep learning and neural networks, HMMs have not faded into obsolescence. In 2021, MIT researchers showcased the enduring importance of HMMs by integrating them into a speaker-adapted HMM-GMM (Hidden Markov Model-Gaussian Mixture Model) Automatic Speech Recognition (ASR) system. This groundbreaking research involved training HMMs using audio data and source language VTT subtitles, leveraging the KALDI framework and Mel-Frequency Cepstral Coefficients (MFCC) features. The outcome was a model that seamlessly combined speech recognition and syntax translation, highlighting the adaptability and effectiveness of HMMs in addressing complex tasks at the intersection of speech processing and natural language understanding.<ref>Salesky E, Wiesner M, Bremerman J, et al. The multilingual tedx corpus for speech recognition and translation[J]. arXiv preprint arXiv:2102.01757, 2021.</ref>


== Future Research ==
== Future Research ==


Hidden Markov Models continue to evolve in tandem with technological advancements. In the realm of speaker recognition, the utilization of speech recognition based on space diversity using distributed multi-microphone is prevalent.<ref>Shimizu Y, Kajita S, Takeda K, et al. Speech recognition based on space diversity using distributed multi-microphone[C]//2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 00CH37100). IEEE, 2000, 3: 1747-1750.</ref>  While they have played a significant role in speaker recognition, recent strides in speech synthesis and deep learning AI have empowered computers to mimic human-like sounds with remarkable precision.


== LLM Review ==
This evolution in speech technology has raised ethical and privacy concerns. As speech recognition technology becomes increasingly integrated into daily life, there is growing apprehension that malicious actors may illicitly collect individuals' voice characteristics and exploit them for fraudulent purposes, such as phone scams. In the foreseeable future, research endeavors may center on the creation of more robust and privacy-conscious methodologies for speaker identification and verification. These efforts aim to mitigate the risks associated with misuse and breaches of privacy and voice data in the context of voice recognition technology.
 
Furthermore, HMMs demonstrate a remarkable ability to handle low-quality and noisy data and address challenges related to speaker independence, surpassing many alternative methods in these domains.<ref>Radha V, Vimala C. A review on speech recognition challenges and approaches[J]. doaj. org, 2012, 2(1): 1-7.</ref> However, practical application reveals that HMM-based speech recognition still faces challenges, particularly in noisy environments, accommodating various speaking styles (e.g. shouting or whispering), and dealing with individuals who have conditions like dysarthria, including children with developing vocal organs. The recognition accuracy in these scenarios unfortunately falls short of the desired level.
 
To overcome these limitations, ongoing research should emphasize innovation and advancement within the HMM technology framework. The goal is to substantially enhance recognition accuracy, especially in challenging scenarios and conditions.


In summary, the dynamic nature of HMMs in the context of voice recognition technology is underscored by evolving ethical and privacy challenges and continuous research efforts to improve recognition accuracy.


== LLM Review ==
ChatGPT 3.5 was used to further expand and refine the first draft of the wiki page. This includes a language and tonal shift to become more academic-sounding. The suggestions and refinement were taken under consideration and then edited by a human student, to better fit the assignment and not overinflate the page with too much information and lower the influence of the human-made draft. Afterwards, ChatGPT was asked to review the entirety of the document, and after parameters and more knowledge was given to ChatGPT that it overlooked, it gave the following short review of the article:<blockquote>''"The article on Hidden Markov Models (HMMs) in speech recognition is a comprehensive and well-structured resource that explores the historical context, key innovations, and the impact of HMMs in the field. It effectively covers various applications of HMMs, such as large vocabulary speech recognition, automatic subtitle generation, continuous speech recognition, and their relevance in contemporary research. The historical background provides valuable context, and the article makes a noteworthy effort to highlight HMMs' contributions beyond speech recognition, notably in DNA sequencing and computational biology. While the content is well-researched and informative, further refinement and citation of specific claims would enhance its academic rigor. Overall, it serves as a valuable reference for those seeking insights into the role of HMMs in speech technology and beyond."''</blockquote>


== References ==
== References ==
Here thus are the references:
<ref>Placeholder Reference</ref>
<references />
<references />

Latest revision as of 08:29, 19 September 2023

Introduction[edit | edit source]

A Hidden Markov Model (HMM) is a temporal probabilistic model characterized by a sequence of hidden states, which are concealed from direct observation but influence observable variables generated by these states. [1] These hidden states adhere to the Markov property, signifying that the future state depends solely on the current state. Given the unavailability of direct access to the underlying states in a specific model, the learning process involves aligning the HMM with observable states.[2][3]

In many real-world scenarios, concealed variables are only discernible through their emitted outcomes. For example, in speech recognition, we often observe the speech signal of a word rather than the specific phoneme states that constitute the hidden states. To decipher the sequence of phonemes leading to a particular word, HMMs facilitate the modeling of the relationship between observable and unobservable variables.[4]

Furthermore, the application of HMMs has exhibited connections with Dynamic Time Warping, offering insights into their utility across diverse domains.[5][6]

Historical Context[edit | edit source]

The origins of HMMs trace back to 1907 when Andrei Markov introduced Markov chains, demonstrating that dependent variables are also subject to the law of large numbers, extending beyond the realm of independent variables.[7] However, it was not until the 1960s that Leonard Baum and Ted Petrie initiated the development of a novel model aimed at estimating the parameters of Markov chains with maximum likelihood. Their work refined the probability equations, enabling the inference of hidden paths within the process.[8][9] Subsequently, Jelinek, Bahl, and Mercer pioneered the application of Markov models in speech recognition, particularly focusing on mitigating speaker-dependent probabilities. This marked a seminal moment in the widespread adoption of HMMs.[10][11]

Over the subsequent decades, substantial advancements and refinements have been made to address inherent challenges in HMMs. Notably, during the 1980s and 1990s[12], innovations such as shared-distribution HMMs in which, instead of having each hidden state in the model associated with its distinct probability distribution, multiple hidden states share a common probability distribution. By doing so, SD-HMMs effectively reduce the number of model parameters, making them more manageable and robust, particularly when dealing with limited training data.[13] Hierarchical Hidden Markov Models (HHMMs) represented a significant evolution from traditional HMMs by introducing a hierarchical structure that empowers individual hidden states to function autonomously and generate sequences of symbols or states, prompting advancements in natural language processing, and becoming crucial in speech recognition and machine translation.[14] Moreover, signal decomposition techniques involving parallel HMMs were developed to address concurrent event recognition challenges, enabling the simultaneous identification and separation of distinct events from complex acoustic signals. This innovation has proven particularly valuable in applications like audio processing, where isolating target sounds from background noise or identifying multiple overlapping events is essential for enhanced signal analysis and interpretation.[15]

In subsequent years, notably during the latter part of the 1980s, HMMs began to play a pivotal role in the domains of DNA sequencing and computational biology. These mathematical models proved instrumental in decoding genetic information, facilitating the analysis of biological data, and unraveling the intricacies of genomic sequences, contributing significantly to the progress of molecular biology and bioinformatics during this period.[16]

Key Innovations[edit | edit source]

Key innovations in the domain of speech recognition harnessing Hidden Markov Models, include: [17]

  • 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.[18] 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.[19]
  • 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.[20][21]
  • Furthermore, before the era of the Deep Learning Revolution and the emergence of end-to-end models, many 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.[22]

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.

Impact on the Field[edit | edit source]

Hidden Markov Models (HMMs) have left an indelible mark on the field of speech recognition, shaping its evolution and expanding its horizons. Their impact is felt across various dimensions of speech processing, underscoring their enduring relevance and adaptability.

  • HMMs have been a cornerstone of large vocabulary speech recognition, revolutionizing the way extensive vocabularies are processed. This inherent capability has been instrumental in advancing the accuracy and efficiency of large vocabulary speech recognition systems, contributing to their widespread adoption.[3]
  • In 1996, Bourlard and Dupont marked a significant milestone by employing HMMs in the domain of automatic subtitle generation. Their pioneering work showcased the versatility of HMMs in transcribing spoken content into textual subtitles. This breakthrough enhanced accessibility and facilitated the dissemination of audiovisual content, making it more inclusive and accessible to diverse audiences.[23]
  • In the domain of continuous speech recognition , these models demonstrate notable expertise in the representation of acoustic features inherent to speech and the formulation of language models conducive to the process of decoding spoken utterances. The incorporation of HMMs within continuous speech recognition systems has played a pivotal role in facilitating the attainment of robust and precise transcription capabilities. Consequently, this integration has substantially broadened the scope of applications within the field of voice recognition and transcription.[24]
  • While the landscape of speech recognition has evolved with the advent of deep learning and neural networks, HMMs have not faded into obsolescence. In 2021, MIT researchers showcased the enduring importance of HMMs by integrating them into a speaker-adapted HMM-GMM (Hidden Markov Model-Gaussian Mixture Model) Automatic Speech Recognition (ASR) system. This groundbreaking research involved training HMMs using audio data and source language VTT subtitles, leveraging the KALDI framework and Mel-Frequency Cepstral Coefficients (MFCC) features. The outcome was a model that seamlessly combined speech recognition and syntax translation, highlighting the adaptability and effectiveness of HMMs in addressing complex tasks at the intersection of speech processing and natural language understanding.[25]

Future Research[edit | edit source]

Hidden Markov Models continue to evolve in tandem with technological advancements. In the realm of speaker recognition, the utilization of speech recognition based on space diversity using distributed multi-microphone is prevalent.[26] While they have played a significant role in speaker recognition, recent strides in speech synthesis and deep learning AI have empowered computers to mimic human-like sounds with remarkable precision.

This evolution in speech technology has raised ethical and privacy concerns. As speech recognition technology becomes increasingly integrated into daily life, there is growing apprehension that malicious actors may illicitly collect individuals' voice characteristics and exploit them for fraudulent purposes, such as phone scams. In the foreseeable future, research endeavors may center on the creation of more robust and privacy-conscious methodologies for speaker identification and verification. These efforts aim to mitigate the risks associated with misuse and breaches of privacy and voice data in the context of voice recognition technology.

Furthermore, HMMs demonstrate a remarkable ability to handle low-quality and noisy data and address challenges related to speaker independence, surpassing many alternative methods in these domains.[27] However, practical application reveals that HMM-based speech recognition still faces challenges, particularly in noisy environments, accommodating various speaking styles (e.g. shouting or whispering), and dealing with individuals who have conditions like dysarthria, including children with developing vocal organs. The recognition accuracy in these scenarios unfortunately falls short of the desired level.

To overcome these limitations, ongoing research should emphasize innovation and advancement within the HMM technology framework. The goal is to substantially enhance recognition accuracy, especially in challenging scenarios and conditions.

In summary, the dynamic nature of HMMs in the context of voice recognition technology is underscored by evolving ethical and privacy challenges and continuous research efforts to improve recognition accuracy.

LLM Review[edit | edit source]

ChatGPT 3.5 was used to further expand and refine the first draft of the wiki page. This includes a language and tonal shift to become more academic-sounding. The suggestions and refinement were taken under consideration and then edited by a human student, to better fit the assignment and not overinflate the page with too much information and lower the influence of the human-made draft. Afterwards, ChatGPT was asked to review the entirety of the document, and after parameters and more knowledge was given to ChatGPT that it overlooked, it gave the following short review of the article:

"The article on Hidden Markov Models (HMMs) in speech recognition is a comprehensive and well-structured resource that explores the historical context, key innovations, and the impact of HMMs in the field. It effectively covers various applications of HMMs, such as large vocabulary speech recognition, automatic subtitle generation, continuous speech recognition, and their relevance in contemporary research. The historical background provides valuable context, and the article makes a noteworthy effort to highlight HMMs' contributions beyond speech recognition, notably in DNA sequencing and computational biology. While the content is well-researched and informative, further refinement and citation of specific claims would enhance its academic rigor. Overall, it serves as a valuable reference for those seeking insights into the role of HMMs in speech technology and beyond."

References[edit | edit source]

  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. ↑ 3.0 3.1 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. ↑ H. Bourlard and S. Dupont, “A new ASR approach based on independent processing and recombination of partial frequency bands.” Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pages 426--429, Philadephia, Pennsylvania, October 1996.
  24. ↑ Young S J, Evermann G, Gales M J F, et al. The HTK book version 3.4 Manual[J]. Cambridge University Engineering Department, Cambridge, UK, 2006.
  25. ↑ Salesky E, Wiesner M, Bremerman J, et al. The multilingual tedx corpus for speech recognition and translation[J]. arXiv preprint arXiv:2102.01757, 2021.
  26. ↑ Shimizu Y, Kajita S, Takeda K, et al. Speech recognition based on space diversity using distributed multi-microphone[C]//2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 00CH37100). IEEE, 2000, 3: 1747-1750.
  27. ↑ Radha V, Vimala C. A review on speech recognition challenges and approaches[J]. doaj. org, 2012, 2(1): 1-7.
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