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Improving low-resource Tibetan end-to-end ASR by multilingual and multilevel unit modeling

Abstract Conventional automatic speech recognition (ASR) and emerging end-to-end (E2E) speech recognition have achieved promising results after being provided with sufficient resources. However, for low-resource language, the current ASR is still challenging. The Lhasa dialect is the most widespread...

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Autors principals: Siqing Qin, Longbiao Wang, Sheng Li, Jianwu Dang, Lixin Pan
Format: Artigo
Idioma:Inglês
Publicat: SpringerOpen 2022-01-01
Col·lecció:EURASIP Journal on Audio, Speech, and Music Processing
Matèries:
Transfer learning
End-to-end
Multilingual speech recognition
Low-resource language
Lhasa dialect
Accés en línia:https://doi.org/10.1186/s13636-021-00233-4
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