65 citations · 78 across the 19 of their papers we have counts for
19 papers
EchoBridge: Long-Tail-Aware ECG-Echocardiography Text Alignment for Echocardiography-Derived Cardiac Findings
Xiaocheng Fang, Jieyi Cai, Guangkun Nie +9
Standardized echocardiography conclusions provide meaningful supervision for learning ECG representations of echocardiography-derived cardiac findings. Global ECG--text alignment m…
UniECG: Understanding and Generating ECG in One Unified Model
Jiarui Jin, Haoyu Wang, Xiang Lan +3
Electrocardiogram (ECG) interpretation is a fundamental skill in medical education, yet students often need more than static examples to connect waveform evidence with diagnostic r…
Transliterated Zero-Shot Domain Adaptation for Automatic Speech Recognition
Han Zhu, Gaofeng Cheng, Qingwei Zhao +1
The performance of automatic speech recognition models often degenerates on domains not covered by the training data. Domain adaptation can address this issue, assuming the availab…
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
Han Zhu, Dongji Gao, Gaofeng Cheng +3
When labeled data is insufficient, semi-supervised learning with the pseudo-labeling technique can significantly improve the performance of automatic speech recognition. However, p…
Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture
Haoran Miao, Gaofeng Cheng, Pengyuan Zhang +1
Recently, there has been increasing progress in end-to-end automatic speech recognition (ASR) architecture, which transcribes speech to text without any pre-trained alignments. One…
Speech Corpora Divergence Based Unsupervised Data Selection for ASR
Changfeng Gao, Gaofeng Cheng, Pengyuan Zhang +1
Selecting application scenarios matching data is important for the automatic speech recognition (ASR) training, but it is difficult to measure the matching degree of the training c…
Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge
Shuhao Deng, Chengfei Li, Jinfeng Bai +6
Code-switching automatic speech recognition becomes one of the most challenging and the most valuable scenarios of automatic speech recognition, due to the code-switching phenomeno…
The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines
Gaofeng Cheng, Yifan Chen, Runyan Yang +9
The conversation scenario is one of the most important and most challenging scenarios for speech processing technologies because people in conversation respond to each other in a c…
Improving Streaming End-to-End ASR on Transformer-based Causal Models with Encoder States Revision Strategies
Zehan Li, Haoran Miao, Keqi Deng +4
There is often a trade-off between performance and latency in streaming automatic speech recognition (ASR). Traditional methods such as look-ahead and chunk-based methods, usually…
Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization
Yifan Chen, Yifan Guo, Qingxuan Li +3
For online speaker diarization, samples arrive incrementally, and the overall distribution of the samples is invisible. Moreover, in most existing clustering-based methods, the tra…
Decoupled Federated Learning for ASR with Non-IID Data
Han Zhu, Jindong Wang, Gaofeng Cheng +2
Automatic speech recognition (ASR) with federated learning (FL) makes it possible to leverage data from multiple clients without compromising privacy. The quality of FL-based ASR c…
Boosting Cross-Domain Speech Recognition with Self-Supervision
Han Zhu, Gaofeng Cheng, Jindong Wang +3
The cross-domain performance of automatic speech recognition (ASR) could be severely hampered due to the mismatch between training and testing distributions. Since the target domai…
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset
Zehui Yang, Yifan Chen, Lei Luo +9
This paper introduces a high-quality rich annotated Mandarin conversational (RAMC) speech dataset called MagicData-RAMC. The MagicData-RAMC corpus contains 180 hours of conversatio…
Improving CTC-based speech recognition via knowledge transferring from pre-trained language models
Keqi Deng, Songjun Cao, Yike Zhang +4
Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2…
Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models
Keqi Deng, Zehui Yang, Shinji Watanabe +3
While Transformers have achieved promising results in end-to-end (E2E) automatic speech recognition (ASR), their autoregressive (AR) structure becomes a bottleneck for speeding up…
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition
Changfeng Gao, Gaofeng Cheng, Pengyuan Zhang
Automatic speech recognition (ASR) has shown rapid advances in recent years but still degrades significantly in far-field and noisy environments. The recent development of self-sup…
Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR
Han Zhu, Li Wang, Jindong Wang +3
Self-supervised pre-training could effectively improve the performance of low-resource automatic speech recognition (ASR). However, existing self-supervised pre-training are task-a…
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture
Haoran Miao, Gaofeng Cheng, Changfeng Gao +2
Recently, Transformer has gained success in automatic speech recognition (ASR) field. However, it is challenging to deploy a Transformer-based end-to-end (E2E) model for online spe…
Utterance-level Permutation Invariant Training with Latency-controlled BLSTM for Single-channel Multi-talker Speech Separation
Lu Huang, Gaofeng Cheng, Pengyuan Zhang +3
Utterance-level permutation invariant training (uPIT) has achieved promising progress on single-channel multi-talker speech separation task. Long short-term memory (LSTM) and bidir…