Speech synthesis has made significant strides thanks to the transition from machine learning
to deep learning models. Contemporary text-to-speech (TTS) models possess the capability …

We introduce a text-to-speech (TTS) model called BASE TTS, which stands for $\textbf {B} $
ig $\textbf {A} $ daptive $\textbf {S} $ treamable TTS with $\textbf {E} $ mergent abilities …

The rapid and wide-scale adoption of AI to generate human speech poses a range of
significant ethical and safety risks to society that need to be addressed. For example, a …

Audio deepfake detection (ADD) is crucial to combat the misuse of speech synthesized by
generative AI models. Existing ADD models suffer from generalization issues to unseen …

Affective computing (AC), like most other areas of computational research, has benefited
tremendously from advances in deep learning (DL). These advances have opened up new …

This study conducted a thorough evaluation of an improved dendritic learning (DL)
framework, focusing specifically on its application in power load forecasting. The objective …

It remains a challenge to effectively control the emotion rendering in text-to-speech (TTS)
synthesis. Prior studies have primarily focused on learning a global prosodic representation …

With recent advancements in generating synthetic speech, tools to generate high-quality
synthetic speech impersonating any human speaker are easily available. Several incidents …

Imbuing machines with the ability to talk has been a longtime pursuit of artificial intelligence
(AI) research. From the very beginning, the community has not only aimed to synthesise high …

Current emotional text-to-speech (TTS) systems face challenges in mimicking a broad
spectrum of human emotions due to the inherent complexity of emotions and limitations in …