Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Wang, Tianzi"'
Autor:
Wang, Tianzi
Effective collaboration between humans and autonomous agents can improve productivity and reduce risks of human operators in safety-critical situations, with autonomous agents working as complementary teammates and lowering physical and mental demand
Externí odkaz:
http://hdl.handle.net/10919/116632
Autor:
Cui, Mingyu, Yang, Yifan, Deng, Jiajun, Kang, Jiawen, Hu, Shujie, Wang, Tianzi, Li, Zhaoqing, Zhang, Shiliang, Chen, Xie, Liu, Xunying
Self-supervised learning (SSL) based discrete speech representations are highly compact and domain adaptable. In this paper, SSL discrete speech features extracted from WavLM models are used as additional cross-utterance acoustic context features in
Externí odkaz:
http://arxiv.org/abs/2409.08797
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, Zhaoqing, Xu, Haoning, Wang, Tianzi, Hu, Shoukang, Jin, Zengrui, Hu, Shujie, Deng, Jiajun, Cui, Mingyu, Geng, Mengzhe, Liu, Xunying
We propose a novel one-pass multiple ASR systems joint compression and quantization approach using an all-in-one neural model. A single compression cycle allows multiple nested systems with varying Encoder depths, widths, and quantization precision s
Externí odkaz:
http://arxiv.org/abs/2406.10160
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:
Wang, Huimeng, Jin, Zengrui, Geng, Mengzhe, Hu, Shujie, Li, Guinan, Wang, Tianzi, Xu, Haoning, Liu, Xunying
Automatic recognition of dysarthric speech remains a highly challenging task to date. Neuro-motor conditions and co-occurring physical disabilities create difficulty in large-scale data collection for ASR system development. Adapting SSL pre-trained
Externí odkaz:
http://arxiv.org/abs/2401.00662
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