no code implementations • 30 Jan 2024 • Yifan Peng, Jinchuan Tian, William Chen, Siddhant Arora, Brian Yan, Yui Sudo, Muhammad Shakeel, Kwanghee Choi, Jiatong Shi, Xuankai Chang, Jee-weon Jung, Shinji Watanabe
In this work, we aim to improve the performance and efficiency of OWSM without extra training data.
no code implementations • 27 Sep 2023 • Xuankai Chang, Brian Yan, Kwanghee Choi, Jeeweon Jung, Yichen Lu, Soumi Maiti, Roshan Sharma, Jiatong Shi, Jinchuan Tian, Shinji Watanabe, Yuya Fujita, Takashi Maekaku, Pengcheng Guo, Yao-Fei Cheng, Pavel Denisov, Kohei Saijo, Hsiu-Hsuan Wang
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling.
1 code implementation • 25 Sep 2023 • Yifan Peng, Jinchuan Tian, Brian Yan, Dan Berrebbi, Xuankai Chang, Xinjian Li, Jiatong Shi, Siddhant Arora, William Chen, Roshan Sharma, Wangyou Zhang, Yui Sudo, Muhammad Shakeel, Jee-weon Jung, Soumi Maiti, Shinji Watanabe
Pre-training speech models on large volumes of data has achieved remarkable success.
no code implementations • 25 Sep 2023 • Jianwei Yu, Hangting Chen, Yanyao Bian, Xiang Li, Yi Luo, Jinchuan Tian, Mengyang Liu, Jiayi Jiang, Shuai Wang
To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically.
1 code implementation • 19 Aug 2023 • Jinchuan Tian, Jianwei Yu, Hangting Chen, Brian Yan, Chao Weng, Dong Yu, Shinji Watanabe
While the vanilla transducer does not have a prior preference for any of the valid paths, this work intends to enforce the preferred paths and achieve controllable alignment prediction.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 14 Oct 2022 • Jinchuan Tian, Brian Yan, Jianwei Yu, Chao Weng, Dong Yu, Shinji Watanabe
Besides predicting the target sequence, a side product of CTC is to predict the alignment, which is the most probable input-long sequence that specifies a hard aligning relationship between the input and target units.
1 code implementation • 5 Jun 2022 • Jinchuan Tian, Jianwei Yu, Chunlei Zhang, Chao Weng, Yuexian Zou, Dong Yu
Experiments conducted on Mandarin-English code-switched speech suggest that the proposed LAE is capable of discriminating different languages in frame-level and shows superior performance on both monolingual and multilingual ASR tasks.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 15 Apr 2022 • Zifeng Zhao, Rongzhi Gu, Dongchao Yang, Jinchuan Tian, Yuexian Zou
Dominant researches adopt supervised training for speaker extraction, while the scarcity of ideally clean corpus and channel mismatch problem are rarely considered.
1 code implementation • 29 Mar 2022 • Jinchuan Tian, Jianwei Yu, Chao Weng, Yuexian Zou, Dong Yu
However, the effectiveness and efficiency of the MBR-based methods are compromised: the MBR criterion is only used in system training, which creates a mismatch between training and decoding; the on-the-fly decoding process in MBR-based methods results in the need for pre-trained models and slow training speeds.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 6 Jan 2022 • Jinchuan Tian, Jianwei Yu, Chao Weng, Yuexian Zou, Dong Yu
Then, the LM score of the hypothesis is obtained by intersecting the generated lattice with an external word N-gram LM.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
1 code implementation • 5 Dec 2021 • Jinchuan Tian, Jianwei Yu, Chao Weng, Shi-Xiong Zhang, Dan Su, Dong Yu, Yuexian Zou
Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 8 Apr 2021 • Jinchuan Tian, Rongzhi Gu, Helin Wang, Yuexian Zou
Transformer-based self-supervised models are trained as feature extractors and have empowered many downstream speech tasks to achieve state-of-the-art performance.
no code implementations • 19 Sep 2019 • Yiheng Huang, Jinchuan Tian, Lei Han, Guangsen Wang, Xingcheng Song, Dan Su, Dong Yu
One important challenge of training an NNLM is to leverage between scaling the learning process and handling big data.