Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Xie, Xurong"'
The Structured Dialogue System, referred to as SuDoSys, is an innovative Large Language Model (LLM)-based chatbot designed to provide psychological counseling. SuDoSys leverages the World Health Organization (WHO)'s Problem Management Plus (PM+) guid
Externí odkaz:
http://arxiv.org/abs/2411.10681
Autor:
Geng, Mengzhe, Xie, Xurong, Deng, Jiajun, Jin, Zengrui, Li, Guinan, Wang, Tianzi, Hu, Shujie, Li, Zhaoqing, Meng, Helen, Liu, Xunying
The application of data-intensive automatic speech recognition (ASR) technologies to dysarthric and elderly adult speech is confronted by their mismatch against healthy and nonaged voices, data scarcity and large speaker-level variability. To this en
Externí odkaz:
http://arxiv.org/abs/2407.06310
Autor:
Hu, Shujie, Xie, Xurong, Geng, Mengzhe, Jin, Zengrui, Deng, Jiajun, Li, Guinan, Wang, Yi, Cui, Mingyu, Wang, Tianzi, Meng, Helen, Liu, Xunying
Self-supervised learning (SSL) based speech foundation models have been applied to a wide range of ASR tasks. However, their application to dysarthric and elderly speech via data-intensive parameter fine-tuning is confronted by in-domain data scarcit
Externí odkaz:
http://arxiv.org/abs/2407.13782
Autor:
Li, Guinan, Deng, Jiajun, Chen, Youjun, Geng, Mengzhe, Hu, Shujie, Li, Zhe, Jin, Zengrui, Wang, Tianzi, Xie, Xurong, Meng, Helen, Liu, Xunying
This paper proposes joint speaker feature learning methods for zero-shot adaptation of audio-visual multichannel speech separation and recognition systems. xVector and ECAPA-TDNN speaker encoders are connected using purpose-built fusion blocks and ti
Externí odkaz:
http://arxiv.org/abs/2406.10152
Autor:
Wang, Tianzi, Xie, Xurong, Li, Zhaoqing, Hu, Shoukang, Jin, Zengrui, Deng, Jiajun, Cui, Mingyu, Hu, Shujie, Geng, Mengzhe, Li, Guinan, Meng, Helen, Liu, Xunying
This paper proposes a novel non-autoregressive (NAR) block-based Attention Mask Decoder (AMD) that flexibly balances performance-efficiency trade-offs for Conformer ASR systems. AMD performs parallel NAR inference within contiguous blocks of output l
Externí odkaz:
http://arxiv.org/abs/2406.10034
Autor:
Jiang, Yicong, Wang, Tianzi, Xie, Xurong, Liu, Juan, Sun, Wei, Yan, Nan, Chen, Hui, Wang, Lan, Liu, Xunying, Tian, Feng
Disordered speech recognition profound implications for improving the quality of life for individuals afflicted with, for example, dysarthria. Dysarthric speech recognition encounters challenges including limited data, substantial dissimilarities bet
Externí odkaz:
http://arxiv.org/abs/2406.09873
Autor:
Jin, Zengrui, Xie, Xurong, Wang, Tianzi, Geng, Mengzhe, Deng, Jiajun, Li, Guinan, Hu, Shujie, Liu, Xunying
Automatic recognition of disordered speech remains a highly challenging task to date due to data scarcity. This paper presents a reinforcement learning (RL) based on-the-fly data augmentation approach for training state-of-the-art PyChain TDNN and en
Externí odkaz:
http://arxiv.org/abs/2312.08641
Autor:
Deng, Jiajun, Li, Guinan, Xie, Xurong, Jin, Zengrui, Cui, Mingyu, Wang, Tianzi, Hu, Shujie, Geng, Mengzhe, Liu, Xunying
Rich sources of variability in natural speech present significant challenges to current data intensive speech recognition technologies. To model both speaker and environment level diversity, this paper proposes a novel Bayesian factorised speaker-env
Externí odkaz:
http://arxiv.org/abs/2306.14608
Autor:
Geng, Mengzhe, Jin, Zengrui, Wang, Tianzi, Hu, Shujie, Deng, Jiajun, Cui, Mingyu, Li, Guinan, Yu, Jianwei, Xie, Xurong, Liu, Xunying
A key challenge in dysarthric speech recognition is the speaker-level diversity attributed to both speaker-identity associated factors such as gender, and speech impairment severity. Most prior researches on addressing this issue focused on using spe
Externí odkaz:
http://arxiv.org/abs/2305.10659
Autor:
Hu, Shujie, Xie, Xurong, Jin, Zengrui, Geng, Mengzhe, Wang, Yi, Cui, Mingyu, Deng, Jiajun, Liu, Xunying, Meng, Helen
Automatic recognition of disordered and elderly speech remains a highly challenging task to date due to the difficulty in collecting such data in large quantities. This paper explores a series of approaches to integrate domain adapted SSL pre-trained
Externí odkaz:
http://arxiv.org/abs/2302.14564