Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Hu, Shujie"'
Autor:
Meng, Lingwei, Zhou, Long, Liu, Shujie, Chen, Sanyuan, Han, Bing, Hu, Shujie, Liu, Yanqing, Li, Jinyu, Zhao, Sheng, Wu, Xixin, Meng, Helen, Wei, Furu
We present MELLE, a novel continuous-valued tokens based language modeling approach for text to speech synthesis (TTS). MELLE autoregressively generates continuous mel-spectrogram frames directly from text condition, bypassing the need for vector qua
Externí odkaz:
http://arxiv.org/abs/2407.08551
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:
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, Jing, 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:
Hu, Shujie, Zhou, Long, Liu, Shujie, Chen, Sanyuan, Hao, Hongkun, Pan, Jing, Liu, Xunying, Li, Jinyu, Sivasankaran, Sunit, Liu, Linquan, Wei, Furu
The recent advancements in large language models (LLMs) have revolutionized the field of natural language processing, progressively broadening their scope to multimodal perception and generation. However, effectively integrating listening capabilitie
Externí odkaz:
http://arxiv.org/abs/2404.00656
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
Large language models (LLMs) have made significant advancements in natural language processing and are concurrently extending the language ability to other modalities, such as speech and vision. Nevertheless, most of the previous work focuses on prom
Externí odkaz:
http://arxiv.org/abs/2401.00246
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:
Li, Guinan, Deng, Jiajun, Geng, Mengzhe, Jin, Zengrui, Wang, Tianzi, Hu, Shujie, Cui, Mingyu, Meng, Helen, Liu, Xunying
Accurate recognition of cocktail party speech containing overlapping speakers, noise and reverberation remains a highly challenging task to date. Motivated by the invariance of visual modality to acoustic signal corruption, an audio-visual multi-chan
Externí odkaz:
http://arxiv.org/abs/2307.02909