62 citations · 172 across the 10 of their papers we have counts for
10 papers
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation
Wen Wu, Bo Li, Chao Zhang +5
The subjective perception of emotion leads to inconsistent labels from human annotators. Typically, utterances lacking majority-agreed labels are excluded when training an emotion…
Parameter Efficient Finetuning for Speech Emotion Recognition and Domain Adaptation
Nineli Lashkarashvili, Wen Wu, Guangzhi Sun +1
Foundation models have shown superior performance for speech emotion recognition (SER). However, given the limited data in emotion corpora, finetuning all parameters of large pre-t…
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation
Wen Wu, Wenlin Chen, Chao Zhang +1
Human annotator simulation (HAS) serves as a cost-effective substitute for human evaluation such as data annotation and system assessment. Human perception and behaviour during hum…
Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for Conversations
Wen Wu, Chao Zhang, Philip C. Woodland
Although automatic emotion recognition (AER) has recently drawn significant research interest, most current AER studies use manually segmented utterances, which are usually unavail…
Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression
Wen Wu, Chao Zhang, Philip C. Woodland
In automatic emotion recognition (AER), labels assigned by different human annotators to the same utterance are often inconsistent due to the inherent complexity of emotion and the…
Self-supervised representations in speech-based depression detection
Wen Wu, Chao Zhang, Philip C. Woodland
This paper proposes handling training data sparsity in speech-based automatic depression detection (SDD) using foundation models pre-trained with self-supervised learning (SSL). An…
Distribution-based Emotion Recognition in Conversation
Wen Wu, Chao Zhang, Philip C. Woodland
Automatic emotion recognition in conversation (ERC) is crucial for emotion-aware conversational artificial intelligence. This paper proposes a distribution-based framework that for…
Climate and Weather: Inspecting Depression Detection via Emotion Recognition
Wen Wu, Mengyue Wu, Kai Yu
Automatic depression detection has attracted increasing amount of attention but remains a challenging task. Psychological research suggests that depressive mood is closely related…
Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors
Wen Wu, Chao Zhang, Xixin Wu +1
Emotion recognition is a key attribute for artificial intelligence systems that need to naturally interact with humans. However, the task definition is still an open problem due to…
Emotion recognition by fusing time synchronous and time asynchronous representations
Wen Wu, Chao Zhang, Philip C. Woodland
In this paper, a novel two-branch neural network model structure is proposed for multimodal emotion recognition, which consists of a time synchronous branch (TSB) and a time asynch…