title: Improving low-resource Tibetan end-to-end ASR by multilingual and multilevel unit modeling
abstract: 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 Tibetan dialect and has a wealth of speakers and transcriptions. Hence, it is meaningful to apply the ASR technique to the Lhasa dialect for historical heritage protection and cultural exchange. Prev
journal: EURASIP Journal on Audio Speech and Music Processing