9 citations · 25 across the 5 of their papers we have counts for
6 papers
Deploying self-supervised learning in the wild for hybrid automatic speech recognition
Mostafa Karimi, Changliang Liu, Kenichi Kumatani +3
Self-supervised learning (SSL) methods have proven to be very successful in automatic speech recognition (ASR). These great improvements have been reported mostly based on highly c…
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
Heming Wang, Yao Qian, Xiaofei Wang +6
Noise robustness is essential for deploying automatic speech recognition (ASR) systems in real-world environments. One way to reduce the effect of noise interference is to employ a…
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes
Rimita Lahiri, Kenichi Kumatani, Eric Sun +1
Multilingual end-to-end(E2E) models have shown a great potential in the expansion of the language coverage in the realm of automatic speech recognition(ASR). In this paper, we aim…
UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Dataset
Chengyi Wang, Yu Wu, Shujie Liu +4
Recently, there has been a vast interest in self-supervised learning (SSL) where the model is pre-trained on large scale unlabeled data and then fine-tuned on a small labeled datas…
Speech-language Pre-training for End-to-end Spoken Language Understanding
Yao Qian, Ximo Bian, Yu Shi +4
End-to-end (E2E) spoken language understanding (SLU) can infer semantics directly from speech signal without cascading an automatic speech recognizer (ASR) with a natural language…
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
Chengyi Wang, Yu Wu, Yao Qian +5
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC le…