16 citations · 45 across the 10 of their papers we have counts for
10 papers
LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech
Jie Chen, Xingchen Song, Zhendong Peng +3
Recent advances in neural text-to-speech (TTS) models bring thousands of TTS applications into daily life, where models are deployed in cloud to provide services for customs. Among…
ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs
Xingchen Song, Di Wu, Binbin Zhang +4
In this paper, we present ZeroPrompt (Figure 1-(a)) and the corresponding Prompt-and-Refine strategy (Figure 3), two simple but effective \textbf{training-free} methods to decrease…
CB-Conformer: Contextual biasing Conformer for biased word recognition
Yaoxun Xu, Baiji Liu, Qiaochu Huang and +4
Due to the mismatch between the source and target domains, how to better utilize the biased word information to improve the performance of the automatic speech recognition model in…
Fast-U2++: Fast and Accurate End-to-End Speech Recognition in Joint CTC/Attention Frames
Chengdong Liang, Xiao-Lei Zhang, BinBin Zhang +5
Recently, the unified streaming and non-streaming two-pass (U2/U2++) end-to-end model for speech recognition has shown great performance in terms of streaming capability, accuracy…
TrimTail: Low-Latency Streaming ASR with Simple but Effective Spectrogram-Level Length Penalty
Xingchen Song, Di Wu, Zhiyong Wu +6
In this paper, we present TrimTail, a simple but effective emission regularization method to improve the latency of streaming ASR models. The core idea of TrimTail is to apply leng…
FusionFormer: Fusing Operations in Transformer for Efficient Streaming Speech Recognition
Xingchen Song, Di Wu, Binbin Zhang +8
The recently proposed Conformer architecture which combines convolution with attention to capture both local and global dependencies has become the \textit{de facto} backbone model…
WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit
Binbin Zhang, Di Wu, Zhendong Peng +7
Recently, we made available WeNet, a production-oriented end-to-end speech recognition toolkit, which introduces a unified two-pass (U2) framework and a built-in runtime to address…
Non-Autoregressive Transformer ASR with CTC-Enhanced Decoder Input
Xingchen Song, Zhiyong Wu, Yiheng Huang +3
Non-autoregressive (NAR) transformer models have achieved significantly inference speedup but at the cost of inferior accuracy compared to autoregressive (AR) models in automatic s…
Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks
Xingchen Song, Guangsen Wang, Zhiyong Wu +4
Self-attention network (SAN) can benefit significantly from the bi-directional representation learning through unsupervised pretraining paradigms such as BERT and XLNet. In this pa…
A Random Gossip BMUF Process for Neural Language Modeling
Yiheng Huang, Jinchuan Tian, Lei Han +4
Neural network language model (NNLM) is an essential component of industrial ASR systems. One important challenge of training an NNLM is to leverage between scaling the learning pr…