In the last few years, the multi-modal emotion recognition has become an important research
issue in the affective computing community due to its wide range of applications that include …

The study of understanding sentiment and emotion in speech is a challenging task in human
multimodal language. However, in certain cases, such as telephone calls, only audio data …

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 …

Speech emotion recognition (SER) has drawn increasing attention for its applications in
human-machine interaction. However, existing SER methods ignore the information gap …

Automatic speech recognition (ASR) and speech emotion recognition (SER) are closely
related in that the acoustic features of speech, such as pitch, tone, and intensity, can vary …

Estimating dimensional emotions, such as activation, valence and dominance, from acoustic
speech signals has been widely explored over the past few years. While accurate estimation …

Although automatic emotion recognition (AER) has recently drawn significant research
interest, most current AER studies use manually segmented utterances, which are usually …

情感识别是实现自然人机交互的必要过程. 然而, 情感数据高昂的采集和标注成本成为了限制
情感识别研究发展的一大瓶颈. 在无标注或有限标注的场景下, 利用知识的跨领域或跨任务迁移 …

Speech emotion recognition (SER) is an important topic in human-computer interactions.
When the input data in training and testing sets comes from different corpora, there is a …

Pre-trained model representations have demonstrated state-of-the-art performance in
speech recognition, natural language processing, and other applications. Speech models …