29 citations · 31 across the 10 of their papers we have counts for
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eess.AS2021
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition
Li Fu, Xiaoxiao Li, Runyu Wang +5
End-to-end Automatic Speech Recognition (ASR) models are usually trained to optimize the loss of the whole token sequence, while neglecting explicit phonemic-granularity supervisio…
eess.AS2020
Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis
Guanghui Xu, Wei Song, Zhengchen Zhang +3
Despite prosody is related to the linguistic information up to the discourse structure, most text-to-speech (TTS) systems only take into account that within each sentence, which ma…
eess.AS2020
Incremental Learning for End-to-End Automatic Speech Recognition
Li Fu, Xiaoxiao Li, Libo Zi +4
In this paper, we propose an incremental learning method for end-to-end Automatic Speech Recognition (ASR) which enables an ASR system to perform well on new tasks while maintainin…