1 code implementation • 8 Nov 2023 • Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Shiliang Zhang, Chong Deng, Yukun Ma, Hai Yu, Jiaqing Liu, Chong Zhang
We find that applying the conventional cross-entropy loss on input speech tokens does not consistently improve the ASR performance over the Loss Masking approach.
1 code implementation • 18 Oct 2023 • Hai Yu, Chong Deng, Qinglin Zhang, Jiaqing Liu, Qian Chen, Wen Wang
Our approach improve $F_1$ of old SOTA by 3. 42 (73. 74 -> 77. 16) and reduces $P_k$ by 1. 11 points (15. 0 -> 13. 89) on WIKI-727K and achieves an average relative reduction of 4. 3% on $P_k$ on WikiSection.
1 code implementation • 22 Jun 2023 • Shurong Chai, Rahul Kumar Jain, Shiyu Teng, Jiaqing Liu, Yinhao Li, Tomoko Tateyama, Yen-Wei Chen
Currently, ongoing research focuses on exploring the effective utilization of these generalized models for specific domains, such as medical imaging.
1 code implementation • 18 May 2023 • Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang
Prior studies diagnose the anisotropy problem in sentence representations from pre-trained language models, e. g., BERT, without fine-tuning.
no code implementations • 27 Mar 2023 • Jiaqing Liu, Chong Deng, Qinglin Zhang, Qian Chen, Wen Wang
We construct and release the first Chinese meeting corpus with manual action item annotations.
1 code implementation • 24 Mar 2023 • Qinglin Zhang, Chong Deng, Jiaqing Liu, Hai Yu, Qian Chen, Wen Wang, Zhijie Yan, Jinglin Liu, Yi Ren, Zhou Zhao
To prompt SLP advancement, we establish a large-scale general Meeting Understanding and Generation Benchmark (MUG) to benchmark the performance of a wide range of SLP tasks, including topic segmentation, topic-level and session-level extractive summarization and topic title generation, keyphrase extraction, and action item detection.
no code implementations • 24 Mar 2023 • Qinglin Zhang, Chong Deng, Jiaqing Liu, Hai Yu, Qian Chen, Wen Wang, Zhijie Yan, Jinglin Liu, Yi Ren, Zhou Zhao
ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SLP applications are critical to improve users' efficiency in grasping important information in meetings.
1 code implementation • 28 Jul 2022 • Hao Sun, Hongyi Wang, Jiaqing Liu, Yen-Wei Chen, Lanfen Lin
Multimodal sentiment analysis and depression estimation are two important research topics that aim to predict human mental states using multimodal data.
1 code implementation • 20 Jul 2021 • Qinglin Zhang, Qian Chen, YaLi Li, Jiaqing Liu, Wen Wang
Evaluations are conducted on the English Wiki-727K document segmentation benchmark, a Chinese Wikipedia-based document segmentation dataset we created, and an in-house Chinese spoken document dataset.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3