Editing Hidden Markov Models in Speech Synthesis (section)

== Impact ==
Hidden Markov Models (HMMs) are versatile probabilistic modeling tools whose emergence has had a wide and far-reaching impact on a variety of fields. Originally developed as a mathematical model for describing the probabilistic relationship between an implied state and a sequence of observations, HMMs have been used in a wide variety of domains, advancing the development of many technologies and applications.

HMMs are powerful probabilistic modeling tools, and their emergence has had a profound impact on natural language processing, speech processing, bioinformatics, and many other fields. First, HMMs play a key role in the field of speech processing. By modeling acoustic signals, HMMs enable computers to understand and Audio-visual synthesis and recognition. This not only facilitates the development of speech recognition technology, but also brings convenience to innovations in the fields of automated voice assistants, voice control, and accessible communication.

Moreover, HMMs produce more natural synthesized speech in speech synthesis, making the synthesized voice closer to the natural voice.

In addition, HMMs are useful in image processing (e.g., Human motion synthesis, Face animation synthesis), audio processing (e.g., Prosodic event recognition, Very low-bitrate speech code), weather prediction, and handwriting recognition ( Online handwriting recognition) are widely used. They provide powerful tools for pattern recognition, object detection, audio event recognition, and face recognition.<ref>Zen H, Nose T, Yamagishi J, et al. The HMM-based speech synthesis system (HTS) version 2.0[J]. SSW, 2007, 6: 294-299.</ref>

In conclusion, the multidisciplinary applications of HMMs show the broad applicability of this probabilistic modeling approach for dealing with time-series data, implicit structures, and incomplete information. They have had a far-reaching impact in several fields, promoting technological innovation and scientific research, and providing powerful tools for the solution of many practical problems. Therefore, HMMs can be considered an important pillar of modern computing and data science.