no code implementations • 24 Jan 2024 • Rhiannon Mogridge, George Close, Robert Sutherland, Thomas Hain, Jon Barker, Stefan Goetze, Anton Ragni
Neural networks have been successfully used for non-intrusive speech intelligibility prediction.
1 code implementation • 24 Oct 2023 • Robert Flynn, Anton Ragni
For the task of speech recognition, the use of more than 30 seconds of acoustic context during training is uncommon, and under-investigated in literature.
no code implementations • 19 Oct 2023 • Wanli Sun, Zehai Tu, Anton Ragni
It also describes how sampling from EBMs can be performed using Langevin Markov Chain Monte-Carlo (MCMC).
no code implementations • 11 Jul 2023 • Yinghao Ma, Ruibin Yuan, Yizhi Li, Ge Zhang, Xingran Chen, Hanzhi Yin, Chenghua Lin, Emmanouil Benetos, Anton Ragni, Norbert Gyenge, Ruibo Liu, Gus Xia, Roger Dannenberg, Yike Guo, Jie Fu
Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech.
no code implementations • 29 Jun 2023 • Robert Flynn, Anton Ragni
While external language models (LMs) are often incorporated into the decoding stage of automated speech recognition systems, these models usually operate with limited context.
1 code implementation • NeurIPS 2023 • Ruibin Yuan, Yinghao Ma, Yizhi Li, Ge Zhang, Xingran Chen, Hanzhi Yin, Le Zhuo, Yiqi Liu, Jiawen Huang, Zeyue Tian, Binyue Deng, Ningzhi Wang, Chenghua Lin, Emmanouil Benetos, Anton Ragni, Norbert Gyenge, Roger Dannenberg, Wenhu Chen, Gus Xia, Wei Xue, Si Liu, Shi Wang, Ruibo Liu, Yike Guo, Jie Fu
This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark.
1 code implementation • 31 May 2023 • Yizhi Li, Ruibin Yuan, Ge Zhang, Yinghao Ma, Xingran Chen, Hanzhi Yin, Chenghao Xiao, Chenghua Lin, Anton Ragni, Emmanouil Benetos, Norbert Gyenge, Roger Dannenberg, Ruibo Liu, Wenhu Chen, Gus Xia, Yemin Shi, Wenhao Huang, Zili Wang, Yike Guo, Jie Fu
Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored.
no code implementations • 5 Dec 2022 • Yizhi Li, Ruibin Yuan, Ge Zhang, Yinghao Ma, Chenghua Lin, Xingran Chen, Anton Ragni, Hanzhi Yin, Zhijie Hu, Haoyu He, Emmanouil Benetos, Norbert Gyenge, Ruibo Liu, Jie Fu
The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL).
1 code implementation • 5 Nov 2022 • Yizhi Li, Ge Zhang, Bohao Yang, Chenghua Lin, Shi Wang, Anton Ragni, Jie Fu
In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups.
no code implementations • 4 Jun 2021 • Zhengxiong Wang, Anton Ragni
Although exact fixed-points inherit the same parallelization and inconsistency issues, this paper shows that approximate fixed-points can be computed in parallel and used consistently in training and inference including tasks such as lattice rescoring.
no code implementations • 8 May 2021 • Sindre André Jacobsen, Anton Ragni
Finally, this paper will show how the proposed model can be augmented with unseen intents without retraining any of the seen ones.
2 code implementations • 25 Oct 2019 • Alexandros Kastanos, Anton Ragni, Mark Gales
This paper examines this limited resource scenario for confidence estimation, a measure commonly used to assess transcription reliability.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 30 Oct 2018 • Anton Ragni, Qiujia Li, Mark Gales, Yu Wang
These errors are not accounted for by the standard confidence estimation schemes and are hard to rectify in the upstream and downstream processing.
4 code implementations • 30 Oct 2018 • Qiujia Li, Preben Ness, Anton Ragni, Mark Gales
The standard approach to mitigate errors made by an automatic speech recognition system is to use confidence scores associated with each predicted word.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 1 Feb 2018 • Yu Wang, Xie Chen, Mark Gales, Anton Ragni, Jeremy Wong
As the combination approaches become more complicated the difference between the phonetic and graphemic systems further decreases.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 18 Aug 2017 • Xie Chen, Xunying Liu, Anton Ragni, Yu Wang, Mark Gales
Instead of using a recurrent unit to capture the complete future word contexts, a feedforward unit is used to model a finite number of succeeding, future, words.
no code implementations • ACL 2017 • Andrey Malinin, Anton Ragni, Kate Knill, Mark Gales
On experiments conducted on data from the Business Language Testing Service (BULATS), the proposed approach is found to outperform GPs and DNNs with MCD in uncertainty-based rejection whilst achieving comparable grading performance.