ROBUST UZBEK ASR AND TTS FOR DIALECTAL AND NOISY SETTINGS

dc.contributor.author	Sukhrob Avezov Sobirovich
dc.date.accessioned	2025-12-29T11:53:25Z
dc.date.issued	2025-09-23
dc.description.abstract	In this article we present a unified recipe for robust Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) for Uzbek under dialectal variation, code-switching, and real-world noise. We combine self-supervised pretraining, dialect-aware lexicons, multi-script text normalization, targeted augmentation, and test-time adaptation. On simulated and field recordings, the ASR reduces WER by large margins; the TTS maintains naturalness and intelligibility across accents and SNRs.
dc.format	application/pdf
dc.identifier.uri	https://americanjournal.org/index.php/ajrhss/article/view/3094
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/18005
dc.language.iso	eng
dc.publisher	American Journals Publishing
dc.relation	https://americanjournal.org/index.php/ajrhss/article/view/3094/2947
dc.rights	https://creativecommons.org/licenses/by-nc/4.0
dc.source	American Journal of Research in Humanities and Social Sciences; Vol. 40 (2025); 1-4
dc.source	2832-8019
dc.subject	Uzbek ASR, TTS, dialect robustness, self-supervised pretraining, code-switching, noise augmentation, multi-script normalization, test-time adaptation.
dc.title	ROBUST UZBEK ASR AND TTS FOR DIALECTAL AND NOISY SETTINGS
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

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