Although social anxiety and depression are common, they are often underdiagnosed and
undertreated, in part due to difficulties identifying and accessing individuals in need of …

Emotions exhibited by a speaker can be detected by analyzing his/her speech, facial
expressions and gestures or by combining these properties. This paper concentrates on …

Long short-term memory (LSTM) is a state-of-the-art network used for different tasks related
to natural language processing (NLP), pattern recognition, and classification. It has been …

Finding an appropriate feature representation for audio data is central to speech emotion
recognition. Most existing audio features rely on hand-crafted feature encoding techniques …

This paper presents a novel approach in continuous emotion prediction that characterizes
dimensional emotion labels jointly with continuous and discretized representations …

Nowadays, especially with the upswing of neural networks, speech synthesis is almost
totally data driven. The goal of this thesis is to provide methods for automatic and …

Speaker variability has been shown to be a significant confounding factor in speech based
emotion classification systems and a number of speaker normalisation techniques have …

In this work we design an approach for automatic feature selection and voice creation for
expressive synthesis. Our approach is guided by two main goals:(1) increasing the flexibility …

In this paper, we propose the use of a Gaussian Probabilistic Linear Discriminant Analysis
(GPLDA) back-end for utterance level emotion classification based on i-vectors representing …

The goal of the study is to predict acoustic features of expressive speech from semantic
vector space representations. Though a lot of successful work was invested in …