Search Results for author: Zhengchen Zhang

Found 6 papers, 0 papers with code

ViDA-MAN: Visual Dialog with Digital Humans

no code implementations26 Oct 2021 Tong Shen, Jiawei Zuo, Fan Shi, Jin Zhang, Liqin Jiang, Meng Chen, Zhengchen Zhang, Wei zhang, Xiaodong He, Tao Mei

We demonstrate ViDA-MAN, a digital-human agent for multi-modal interaction, which offers realtime audio-visual responses to instant speech inquiries.

speech-recognition Speech Recognition +2

SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition

no code implementations8 Oct 2021 Li Fu, Xiaoxiao Li, Runyu Wang, Lu Fan, Zhengchen Zhang, Meng Chen, Youzheng Wu, Xiaodong He

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 supervision.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis

no code implementations6 Nov 2020 Guanghui Xu, Wei Song, Zhengchen Zhang, Chao Zhang, Xiaodong He, BoWen Zhou

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 makes it challenging when converting a paragraph of texts into natural and expressive speech.

Sentence Sentence Embeddings +1

Incremental Learning for End-to-End Automatic Speech Recognition

no code implementations11 May 2020 Li Fu, Xiaoxiao Li, Libo Zi, Zhengchen Zhang, Youzheng Wu, Xiaodong He, BoWen Zhou

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 maintaining the performance on its originally learned ones.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Transition-based Parsing with Context Enhancement and Future Reward Reranking

no code implementations15 Dec 2016 Fugen Zhou, Fuxiang Wu, Zhengchen Zhang, Minghui Dong

This paper presents a novel reranking model, future reward reranking, to re-score the actions in a transition-based parser by using a global scorer.

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