no code implementations • Findings (EMNLP) 2021 • Fuwei Cui, Hui Di, Hongjie Ren, Kazushige Ouchi, Ze Liu, Jinan Xu
Generative conversation systems tend to produce meaningless and generic responses, which significantly reduce the user experience.
no code implementations • CCL 2020 • Pengfei Chen, Lina Wang, Hui Di, Kazushige Ouchi, Lvhong Wang
In contrast to existing quantization with low precision data format and projection layer, we propose a novel method based on shared labels, which focuses on compressing the fully-connected layer before Softmax for models with a huge number of labels in TTS polyphone selection.
no code implementations • 15 Sep 2023 • Yiming Li, Xiangdong Wang, Hong Liu, Rui Tao, Long Yan, Kazushige Ouchi
Then, the local consistency is adopted to encourage the model to leverage local features for frame-level predictions, and the global consistency is applied to force features to align with global prototypes through a specially designed contrastive loss.
no code implementations • 23 Aug 2023 • Zhifang Guo, Jianguo Mao, Rui Tao, Long Yan, Kazushige Ouchi, Hong Liu, Xiangdong Wang
To address this issue, we propose a novel model that enhances the controllability of existing pre-trained text-to-audio models by incorporating additional conditions including content (timestamp) and style (pitch contour and energy contour) as supplements to the text.
1 code implementation • 18 Oct 2022 • Yiming Li, Zhifang Guo, Zhirong Ye, Xiangdong Wang, Hong Liu, Yueliang Qian, Rui Tao, Long Yan, Kazushige Ouchi
For the frame-wise model, the ICT-TOSHIBA system of DCASE 2021 Task 4 is used.
2 code implementations • 12 Oct 2021 • Rui Tao, Long Yan, Kazushige Ouchi, Xiangdong Wang
The recently proposed Mean Teacher method, which exploits large-scale unlabeled data in a self-ensembling manner, has achieved state-of-the-art results in several semi-supervised learning benchmarks.
1 code implementation • 5 Oct 2021 • Zhirong Ye, Xiangdong Wang, Hong Liu, Yueliang Qian, Rui Tao, Long Yan, Kazushige Ouchi
A critical issue with the frame-based model is that it pursues the best frame-level prediction rather than the best event-level prediction.