MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining

no code implementations • 2 Mar 2024 • Yanchao Tan, Hang Lv, Xinyi Huang, Jiawei Zhang, Shiping Wang, Carl Yang

Traditional Graph Neural Networks (GNNs), which are commonly used for modeling attributed graphs, need to be re-trained every time when applied to different graph tasks and datasets.

Graph Mining

Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representation

no code implementations • 22 Oct 2023 • Kun Wei, Bei Li, Hang Lv, Quan Lu, Ning Jiang, Lei Xie

By introducing both cross-modal and conversational representations into the decoder, our model retains context over longer sentences without information loss, achieving relative accuracy improvements of 8. 8% and 23% on Mandarin conversation datasets HKUST and MagicData-RAMC, respectively, compared to the standard Conformer model.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit

3 code implementations • 29 Mar 2022 • BinBin Zhang, Di wu, Zhendong Peng, Xingchen Song, Zhuoyuan Yao, Hang Lv, Lei Xie, Chao Yang, Fuping Pan, Jianwei Niu

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 the streaming and non-streaming decoding modes in a single model.

Language Modelling speech-recognition +1

WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition

1 code implementation • 7 Oct 2021 • BinBin Zhang, Hang Lv, Pengcheng Guo, Qijie Shao, Chao Yang, Lei Xie, Xin Xu, Hui Bu, Xiaoyu Chen, Chenchen Zeng, Di wu, Zhendong Peng

In this paper, we present WenetSpeech, a multi-domain Mandarin corpus consisting of 10000+ hours high-quality labeled speech, 2400+ hours weakly labeled speech, and about 10000 hours unlabeled speech, with 22400+ hours in total.

Label Error Detection Optical Character Recognition +4

Wake Word Detection with Streaming Transformers

no code implementations • 8 Feb 2021 • Yiming Wang, Hang Lv, Daniel Povey, Lei Xie, Sanjeev Khudanpur

Modern wake word detection systems usually rely on neural networks for acoustic modeling.

Wake Word Detection with Alignment-Free Lattice-Free MMI

1 code implementation • 17 May 2020 • Yiming Wang, Hang Lv, Daniel Povey, Lei Xie, Sanjeev Khudanpur

Always-on spoken language interfaces, e. g. personal digital assistants, rely on a wake word to start processing spoken input.

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

1 code implementation • 18 Sep 2019 • Yiming Wang, Tongfei Chen, Hainan Xu, Shuoyang Ding, Hang Lv, Yiwen Shao, Nanyun Peng, Lei Xie, Shinji Watanabe, Sanjeev Khudanpur

We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit fairseq.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

The NNI Query-by-Example System for MediaEval 2015

no code implementations • MediaEval 2015 Workshop 2015 • Jingyong Hou, Van Tung Pham, Cheung-Chi Leung, Lei Wang, HaiHua Xu, Hang Lv, Lei Xie, Zhonghua Fu, Chongjia Ni, Xiong Xiao, Hongjie Chen, Shaofei Zhang, Sining Sun, Yougen Yuan, Pengcheng Li, Tin Lay Nwe, Sunil Sivadas, Bin Ma, Eng Siong Chng, Haizhou Li

This paper describes the system developed by the NNI team for the Query-by-Example Search on Speech Task (QUESST) in the MediaEval 2015 evaluation.

Keyword Spotting

The NNI Query-by-Example System for MediaEval 2014

no code implementations • 16 Oct 2014 • Peng Yang, HaiHua Xu, Xiong Xiao, Lei Xie, Cheung-Chi Leung, Hongjie Chen, JIA YU, Hang Lv, Lei Wang, Su Jun Leow, Bin Ma, Eng Siong Chng, Haizhou Li

For both symbolic and DTW search, partial sequence matching is performed to reduce missing rate, especially for query type 2 and 3.

Dynamic Time Warping Keyword Spotting +1