Emotional voice conversion: Theory, databases and esd
In this paper, we first provide a review of the state-of-the-art emotional voice conversion
research, and the existing emotional speech databases. We then motivate the development …
research, and the existing emotional speech databases. We then motivate the development …
[HTML][HTML] KBES: A dataset for realistic Bangla speech emotion recognition with intensity level
Abstract Speech Emotion Recognition (SER) identifies and categorizes emotional states by
analyzing speech signals. SER is an emerging research area using machine learning and …
analyzing speech signals. SER is an emerging research area using machine learning and …
DeepEZ: a graph convolutional network for automated epileptogenic zone localization from resting-state fMRI connectivity
Objective: Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up
and therapeutic planning in medication refractory epilepsy. In this paper, we present the first …
and therapeutic planning in medication refractory epilepsy. In this paper, we present the first …
[HTML][HTML] Map** discrete emotions in the dimensional space: An acoustic approach
A frequently used procedure to examine the relationship between categorical and
dimensional descriptions of emotions is to ask subjects to place verbal expressions …
dimensional descriptions of emotions is to ask subjects to place verbal expressions …
Emotions and virality: Social transmission of political messages on Twitter
Drawing on previous literature that valence and arousal constitute the fundamental
properties of emotions and that emotional content is a determinant of social transmission …
properties of emotions and that emotional content is a determinant of social transmission …
Non-parallel emotion conversion using a deep-generative hybrid network and an adversarial pair discriminator
We introduce a novel method for emotion conversion in speech that does not require
parallel training data. Our approach loosely relies on a cycle-GAN schema to minimize the …
parallel training data. Our approach loosely relies on a cycle-GAN schema to minimize the …
You're Not You When You're Angry: Robust Emotion Features Emerge by Recognizing Speakers
The robustness of an acoustic emotion recognition system hinges on first having access to
features that represent an acoustic input signal. These representations should abstract …
features that represent an acoustic input signal. These representations should abstract …
[PDF][PDF] A Multi-Speaker Emotion Morphing Model Using Highway Networks and Maximum Likelihood Objective.
We introduce a new model for emotion conversion in speech based on highway neural
networks. Our model uses the contextual pitch, energy and spectral information of a source …
networks. Our model uses the contextual pitch, energy and spectral information of a source …
Emotion modelling for speech generation
K Zhou - 2023 - search.proquest.com
Speech generation aims to synthesize human-like voices from the input of text or speech.
Current speech generation techniques can generate high quality, natural-sounding speech …
Current speech generation techniques can generate high quality, natural-sounding speech …
A comparative study of data augmentation techniques for deep learning based emotion recognition
Automated emotion recognition in speech is a long-standing problem. While early work on
emotion recognition relied on hand-crafted features and simple classifiers, the field has now …
emotion recognition relied on hand-crafted features and simple classifiers, the field has now …