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Hidden Markov Models in Speech Synthesis
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=== Early speech synthesis Development === Speech synthesis, also known as TTS (text-to-speech), refers to the artificial generation of human speech through computer technology. It is the automated process of converting written text into an acoustic speech signal. The historical perspective on speech synthesis research reveals that the earliest systems, often referred to as "têtes parlantes" or talking heads, emerged in the eighteenth century <ref>Kuligowska, K., Kisielewicz, P., & Włodarz, A. (2018). Speech synthesis systems: disadvantages and limitations. ''Int J Res Eng Technol (UAE)'', ''7'', 234-239.</ref>. The mechanical nature of early systems limited their ability to reproduce speech that closely resembled natural human speech. Before HMMs became involved in speech synthesis, several techniques were employed to generate synthetic speech. These techniques included formant synthesis, articulatory synthesis, oncatenative synthesis and unit selection synthesis <ref>Tabet, Y., & Boughazi, M. (2011, May). Speech synthesis techniques. A survey. In ''International Workshop on Systems, Signal Processing and their Applications, WOSSPA'' (pp. 67-70). IEEE.</ref>. ==== Formant Synthesis ==== Formant synthesis involves the use of resonance structures called formants to generate speech. In some cases, a combination of parallel and cascade resonators is employed. A notable example is the '''Klatt synthesizer''', which utilized 39 parameters updated every 5 milliseconds. While formant synthesis can produce intelligible speech, it is often considered less natural than other methods. ==== Articulatory Synthesis ==== Articulatory synthesis seeks to generate speech by directly modeling the movements of human articulators. This method offers the potential for high-quality speech but is challenging to implement. Articulatory control parameters include various factors like lip aperture, tongue position, and height. However, acquiring accurate articulatory data, often through x-ray photography, and finding a balance between precision and simplicity are challenges. The results of articulatory synthesis may not always match the quality of other synthesis methods. ==== Concatenative Synthesis ==== Concatenative synthesis addresses the difficulty in generating speech parameters from input text specifications. It employs a data-driven approach by connecting natural, prerecorded speech units, such as words, syllables, or diphones. Diphones are widely used and start in the middle of one phoneme and extend to the middle of the following one, capturing coarticulation. Building a diphone inventory involves recording all phonemes within possible contexts and labeling and segmenting diphones <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>. The pitch and duration of each diphone must be adjusted to match the prosody part of the specification. This approach balances memory requirements, complexity, and naturalness. ==== Unit Selection Synthesis ==== During the 1990s, unit selection synthesis, also known as corpus-based concatenative synthesis, emerged, driven by the growing power of computer technology and the increasing availability of speech and linguistic resources <ref>Hunt, A. J., & Black, A. W. (1996). Unit selection in a concatenative speech synthesis system using a large speech database. ''1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings'', ''1'', 373–376. <nowiki>https://doi.org/10.1109/ICASSP.1996.541110</nowiki></ref>. Unit selection synthesis addresses issues associated with prosodic modifications in concatenative synthesis. It stores multiple instances of each unit with varying prosodies in the unit inventory, allowing for better matching to the target prosody. An algorithm selects the units that best match the target specification based on minimizing target cost and join cost functions. However, Unit selection synthesis has limitations in terms of expressiveness, customization, and prosody control due to its reliance on recorded speech units and extensive databases <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>.
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