Editing Hidden Markov Models in Speech Synthesis (section)

=== HMM-based speech synthesis systems(HTS) ===
In traditional speech synthesis systems that rely on the selection and concatenation of acoustical units, the need for a substantial volume of speech data to encompass various voice characteristics can be a significant challenge. Collecting and storing such a large dataset can be complex and resource-intensive. To address this issue and construct speech synthesis systems capable of generating a wide range of voice characteristics, the HMM-based speech synthesis system (HTS) was introduced <ref>Tokuda, K., Zen, H., & Black, A. W. (2002, September). An HMM-based speech synthesis system applied to English. In ''IEEE speech synthesis workshop'' (pp. 227-230). Santa Monica: IEEE.</ref>. 

HMM-based speech synthesis, often referred to as "HTS", represents a significant advancement in the realm of Text-to-Speech technology. Emerging in the late 1990s, this data-driven approach provides a novel means of achieving precise control over speech variations. By modeling various acoustic parameters using a time-series stochastic generative model, HMM-based speech synthesis offers a powerful alternative to traditional unit selection and concatenation methods. One of its key advantages is the ability to perform voice alterations without the need for extensive databases, while maintaining a level of quality that rivals the traditional approaches <ref>Kayte, S., Mundada, M., & Gujrathi, J. (2015). Hidden Markov Model based Speech Synthesis: A Review. ''International Journal of Computer Applications'', ''130''(3), 35–39. <nowiki>https://doi.org/10.5120/ijca2015906965</nowiki></ref>. 

This flexibility in voice modification, combined with its capacity to generate natural and intelligible speech, has contributed to the growing popularity and success of HMM-based speech synthesis in recent years.

The adoption of HMM-based speech synthesis has been facilitated by well-established machine learning algorithms, many of which originated in the field of automatic speech recognition (ASR). These algorithms, such as Baum-Welch, Viterbi, and clustering methods, have proven their efficiency and effectiveness. Additionally, the availability of open-source toolkits covering essential areas like text analysis, signal processing, and HMMs has contributed to the widespread use of this technology in both academic and commercial organizations.

This surge in interest is underscored by the fact that approximately 76% of the papers presented at INTERSPEECH 2012, a prominent international conference on speech information processing, utilized HMM-based approaches. This widespread adoption strongly reinforces the need for and potential of this innovative approach, confirming its status as a pivotal technology in the field of speech synthesis <ref>Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., & Oura, K. (2013). Speech Synthesis Based on Hidden Markov Models. ''Proceedings of the IEEE'', ''101''(5), 1234–1252. <nowiki>https://doi.org/10.1109/JPROC.2013.2251852</nowiki></ref>.