6 accepted papers
Deep learning models trained on audio-visual data have been successfully used to achieve state-of-the-art performance for emotion recognition. In particular, models trained with multitask learning have shown additional performance improvements. However, such multitask models entangle information bet…
Conventional models for emotion recognition from speech signal are trained in supervised fashion using speech utterances with emotion labels. In this study we hypothesize that speech signal depends on multiple latent variables including the emotional state, age, gender, and speech content. We propos…
Expressive speech introduces variations in the acoustic features affecting the performance of speech technology such as speaker verification systems. It is important to identify the range of emotions for which we can reliably estimate speaker verification tasks. This paper studies the performance of…
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…
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…
Extractive speech summarization methods generally operate as a binary classifier deciding if a sentence belongs to the summary or not. However, it is well known that even human annotators do not agree on selecting most summary sentences. In this paper, we take a probabilistic view of the summarizati…