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 …

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

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 …

Human emotion understanding is pivotal in making conversational technology mainstream.
We view speech emotion understanding as a perception task which is a more realistic …

Receiving navigation directions and relevant information through appropriate channels is
crucial for individuals with visual impairments when they use navigation assistant systems …

Speech emotion conversion is the task of converting the expressed emotion of a spoken
utterance to a target emotion while preserving the lexical content and speaker identity. While …

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

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 …

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 …

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