500 citations · 615 across the 12 of their papers we have counts for
12 papers
Improving the fusion of acoustic and text representations in RNN-T
Chao Zhang, Bo Li, Zhiyun Lu +2
The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR). To estimate the output dist…
Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition
Xianrui Zheng, Chao Zhang, Philip C. Woodland
Language models (LMs) pre-trained on massive amounts of text, in particular bidirectional encoder representations from Transformers (BERT), generative pre-training (GPT), and GPT-2…
Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition
Guangzhi Sun, Chao Zhang, Philip C. Woodland
Contextual knowledge is important for real-world automatic speech recognition (ASR) applications. In this paper, a novel tree-constrained pointer generator (TCPGen) component is pr…
Combining Frame-Synchronous and Label-Synchronous Systems for Speech Recognition
Qiujia Li, Chao Zhang, Philip C. Woodland
Commonly used automatic speech recognition (ASR) systems can be classified into frame-synchronous and label-synchronous categories, based on whether the speech is decoded on a per-…
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training
Adnan Haider, Chao Zhang, Florian L. Kreyssig +1
This paper presents a novel natural gradient and Hessian-free (NGHF) optimisation framework for neural network training that can operate efficiently in a distributed manner. It rel…
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…
Emotion recognition by fusing time synchronous and time asynchronous representations
Wen Wu, Chao Zhang, Philip C. Woodland
In this paper, a novel two-branch neural network model structure is proposed for multimodal emotion recognition, which consists of a time synchronous branch (TSB) and a time asynch…
Combination of Deep Speaker Embeddings for Diarisation
Guangzhi Sun, Chao Zhang, Phil Woodland
Significant progress has recently been made in speaker diarisation after the introduction of d-vectors as speaker embeddings extracted from neural network (NN) speaker classifiers…
Neural Kalman Filtering for Speech Enhancement
Wei Xue, Gang Quan, Chao Zhang +3
Statistical signal processing based speech enhancement methods adopt expert knowledge to design the statistical models and linear filters, which is complementary to the deep neural…
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications
Chao Zhang, Zichao Yang, Xiaodong He +1
Deep learning methods have revolutionized speech recognition, image recognition, and natural language processing since 2010. Each of these tasks involves a single modality in their…
Improved Large-margin Softmax Loss for Speaker Diarisation
Yassir Fathullah, Chao Zhang, Philip C. Woodland
Speaker diarisation systems nowadays use embeddings generated from speech segments in a bottleneck layer, which are needed to be discriminative for unseen speakers. It is well-know…
Multi-Span Acoustic Modelling using Raw Waveform Signals
Patrick von Platen, Chao Zhang, Philip Woodland
Traditional automatic speech recognition (ASR) systems often use an acoustic model (AM) built on handcrafted acoustic features, such as log Mel-filter bank (FBANK) values. Recent s…