{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Laptev A"],"funding":["ITMO University"],"pagination":["3063"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8124527"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(9)"],"pubmed_abstract":["With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. For on-device speech recognition tasks, researchers and industry prefer end-to-end ASR systems as they can be made resource-efficient while maintaining a higher quality compared to hybrid systems. However, building end-to-end models requires a significant amount of speech data. Personalization, which is mainly handling out-of-vocabulary (OOV) words, is another challenging task associated with speech assistants. In this work, we consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel Georgian tasks. We propose a method of dynamic acoustic unit augmentation ba"],"journal":["Sensors (Basel, Switzerland)"],"pubmed_title":["Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition."],"pmcid":["PMC8124527"],"funding_grant_id":["620173"],"pubmed_authors":["Mitrofanov A","Medennikov I","Matveev Y","Laptev A","Andrusenko A","Podluzhny I"],"additional_accession":[]},"is_claimable":false,"name":"Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition.","description":"With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. For on-device speech recognition tasks, researchers and industry prefer end-to-end ASR systems as they can be made resource-efficient while maintaining a higher quality compared to hybrid systems. However, building end-to-end models requires a significant amount of speech data. Personalization, which is mainly handling out-of-vocabulary (OOV) words, is another challenging task associated with speech assistants. In this work, we consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel Georgian tasks. We propose a method of dynamic acoustic unit augmentation ba","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Apr","modification":"2025-04-04T11:58:17.869Z","creation":"2022-02-10T10:46:18.508Z"},"accession":"S-EPMC8124527","cross_references":{"pubmed":["33924798"],"doi":["10.3390/s21093063"]}}