Editing Hidden Markov Models in Speech Synthesis (section)

=== Adoption of Hidden Markov Models (HMMs): ===
[[Hidden Markov Models]] (HMMs), originally developed for speech recognition, have recently gained attention for their potential in speech synthesis applications. The fundamental theory behind HMMs owes its roots to groundbreaking work by Baum and his colleagues. Stratonovich (1960) is credited with earlier work in this field, proposing an optimal nonlinear filtering model grounded in the theory of conditional Markov processes. 

A significant advancement in applying HMMs to speech was achieved by Rabiner in 1989. His work led to the formulation of a statistical method for representing speech, resulting in a successful implementation of an HMM system capable of handling discrete or continuous density parameter distributions. These collective contributions have laid the foundation for the exploration of HMMs in speech synthesis, highlighting their versatility in modeling and generating speech, a field in which they were not initially envisioned but have now become a pivotal technology <ref>Awad, M., & Khanna, R. (2015). Hidden Markov Model. In M. Awad & R. Khanna, ''Efficient Learning Machines'' (pp. 81–104). Apress. <nowiki>https://doi.org/10.1007/978-1-4302-5990-9_5</nowiki></ref>.