7 accepted papers
The ability to identify speech with similar emotional content is valuable to many applications, including speech retrieval, surveillance, and emotional speech synthesis. While current formulations in speech emotion recognition based on classification or regression are not appropriate for this task,…
Deep neural networks have been proven to be very effective in various classification problems and show great promise for emotion recognition from speech. Studies have proposed various architectures that further improve the performance of emotion recognition systems. However, there are still various…
When emotion recognition systems are used in new domains, the classification performance usually drops due to mismatches between training and testing conditions. Annotations of new data in the new domain is expensive and time demanding. Therefore, it is important to design strategies that efficientl…
The performance of speech emotion classifiers greatly degrade when the training conditions do not match the testing conditions. This problem is observed in cross-corpora evaluations, even when the corpora are similar. The lack of generalization is particularly problematic when the emotion classifier…
Research on automatic speech summarization typically focuses on optimizing objective evaluation criteria, such as the ROUGE metric, which depend on word and phrase overlaps between automatic and manually generated summary documents. However, the actual quality of the speech summarizer largely depend…
Emotional descriptors collected from perceptual evaluations are important in the study of emotions. Many studies on emotion recognition depend on these labels to train classifiers. The reliability of the emotion descriptors vary with the number and quality of the raters. Conducting perceptual evalua…
One of the main barriers in the deployment of speech emotion recognition systems in real applications is the lack of generalization of the emotion classifiers. The recognition performance achieved in controlled recordings drops when the models are tested with different speakers, channels, environmen…