Zobrazeno 1 - 10
of 177
pro vyhledávání: '"Lu, Xugang"'
Autor:
Hung, Kuo-Hsuan, Wang, Kuan-Chen, Liu, Kai-Chun, Chen, Wei-Lun, Lu, Xugang, Tsao, Yu, Lin, Chii-Wann
Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical interference or signal wandering, which reduces diagnostic accuracy. Va
Externí odkaz:
http://arxiv.org/abs/2409.18828
Domain gap often degrades the performance of speaker verification (SV) systems when the statistical distributions of training data and real-world test speech are mismatched. Channel variation, a primary factor causing this gap, is less addressed than
Externí odkaz:
http://arxiv.org/abs/2409.09396
Knowledge distillation (KD) is widely used in audio tasks, such as speaker verification (SV), by transferring knowledge from a well-trained large model (the teacher) to a smaller, more compact model (the student) for efficiency and portability. Exist
Externí odkaz:
http://arxiv.org/abs/2409.09389
Transferring linguistic knowledge from a pretrained language model (PLM) to an acoustic model has been shown to greatly improve the performance of automatic speech recognition (ASR). However, due to the heterogeneous feature distributions in cross-mo
Externí odkaz:
http://arxiv.org/abs/2409.02239
Recent research in speaker verification has increasingly focused on achieving robust and reliable recognition under challenging channel conditions and noisy environments. Identifying speakers in radio communications is particularly difficult due to i
Externí odkaz:
http://arxiv.org/abs/2406.10956
Autor:
Lee, Cho-Yuan, Wang, Kuan-Chen, Liu, Kai-Chun, Wang, Yu-Te, Lu, Xugang, Yeh, Ping-Cheng, Tsao, Yu
In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the qua
Externí odkaz:
http://arxiv.org/abs/2402.05482
Speech emotion recognition (SER) performance deteriorates significantly in the presence of noise, making it challenging to achieve competitive performance in noisy conditions. To this end, we propose a multi-level knowledge distillation (MLKD) method
Externí odkaz:
http://arxiv.org/abs/2312.13556
Multi-talker overlapped speech recognition remains a significant challenge, requiring not only speech recognition but also speaker diarization tasks to be addressed. In this paper, to better address these tasks, we first introduce speaker labels into
Externí odkaz:
http://arxiv.org/abs/2312.10959
Domain shift poses a significant challenge in cross-domain spoken language recognition (SLR) by reducing its effectiveness. Unsupervised domain adaptation (UDA) algorithms have been explored to address domain shifts in SLR without relying on class la
Externí odkaz:
http://arxiv.org/abs/2310.13471
Due to the modality discrepancy between textual and acoustic modeling, efficiently transferring linguistic knowledge from a pretrained language model (PLM) to acoustic encoding for automatic speech recognition (ASR) still remains a challenging task.
Externí odkaz:
http://arxiv.org/abs/2309.16093