4 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,…
Studies have shown that ranking emotional attributes through preference learning methods has significant advantages over conventional emotional classification/regression frameworks. Preference learning is particularly appealing for retrieval tasks, where the goal is to identify speech conveying targ…
A speech emotion retrieval system aims to detect a subset of data with specific expressive content. Preference learning represents an appealing framework to rank speech samples in terms of continuous attributes such as arousal and valence. The training of ranking classifiers usually requires pairwis…
A common approach to recognize emotion from speech is to estimate multiple acoustic features at sentence or turn level. These features are derived independent of the underlying lexical content. Studies have demonstrated that lexical dependent models improve emotion recognition accuracy. However, cur…