Abstract
Speech technologies such as text-to-speech (TTS) and speech-to-text (STT) are becoming increasingly applicable. Significant improvements in their quality are driven by advancements in deep machine learning. The ability of devices to deeply understand human speech and generate appropriate responses is a hallmark of AI capabilities. Developing speech technology requires extensive speech and language resources, which is why many languages with smaller speaker bases lag behind widely spoken languages in the development of speech technologys. Prior to the deep learning (DL) paradigm, hidden Markov models (HMM) and probabilistic approaches dominated speech technology development. This paper reviews the challenges and solutions in TTS and STT development for Serbian, highlighting the transition from HMM to DL. It also explores the future prospects of speech technology development for under-resourced languages and its role in preserving these languages.
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Acknowledgments
This research was supported by the Science Fund of the Republic of Serbia, Grant No. 7449, Multimodal multilingual human-machine speech communication, AI-SPEAK, and by the Ministry of Science, Technological Development and Innovation (Contract No. 451–03-65/2024–03/200156) and the Faculty of Technical Sciences, University of Novi Sad through project “Scientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad” (No. 01–3394/1).
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Sečujski, M. et al. (2025). Retrospective and Perspectives of TTS & STT Technology Development and Implementation for South Slavic Under-Resourced Languages. In: Karpov, A., Delić, V. (eds) Speech and Computer. SPECOM 2024. Lecture Notes in Computer Science(), vol 15299. Springer, Cham. https://doi.org/10.1007/978-3-031-77961-9_2
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