no code implementations • 15 Sep 2022 • Vincent Karas, Andreas Triantafyllopoulos, Meishu Song, Björn W. Schuller
Vocal bursts play an important role in communicating affect, making them valuable for improving speech emotion recognition.
no code implementations • 22 Jun 2022 • Meishu Song, Zijiang Yang, Andreas Triantafyllopoulos, Xin Jing, Vincent Karas, Xie Jiangjian, Zixing Zhang, Yamamoto Yoshiharu, Bjoern W. Schuller
We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challenge.
no code implementations • 20 Jun 2022 • Andreas Triantafyllopoulos, Anastasia Semertzidou, Meishu Song, Florian B. Pokorny, Björn W. Schuller
As compared to other existing COVID-19 sound datasets, the unique feature of the COVYT dataset is that it comprises both COVID-19 positive and negative samples from all 65 speakers.
1 code implementation • 14 Jun 2022 • Andreas Triantafyllopoulos, Meishu Song, Zijiang Yang, Xin Jing, Björn W. Schuller
In this work, we explore a novel few-shot personalisation architecture for emotional vocalisation prediction.
no code implementations • 31 Mar 2022 • Xin Jing, Shuo Liu, Emilia Parada-Cabaleiro, Andreas Triantafyllopoulos, Meishu Song, Zijiang Yang, Björn W. Schuller
Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission.
no code implementations • 29 Mar 2022 • Zijiang Yang, Xin Jing, Andreas Triantafyllopoulos, Meishu Song, Ilhan Aslan, Björn W. Schuller
Emotional voice conversion (EVC) focuses on converting a speech utterance from a source to a target emotion; it can thus be a key enabling technology for human-computer interaction applications and beyond.
no code implementations • 30 Apr 2020 • Jing Han, Kun Qian, Meishu Song, Zijiang Yang, Zhao Ren, Shuo Liu, Juan Liu, Huaiyuan Zheng, Wei Ji, Tomoya Koike, Xiao Li, Zixing Zhang, Yoshiharu Yamamoto, Björn W. Schuller
In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.