Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

The building sector accounts for 36% of the total global energy usage and 40% of
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …

Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

Emotional voice conversion aims to transform emotional prosody in speech while preserving
the linguistic content and speaker identity. Prior studies show that it is possible to …

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 …

Speech technology is not appropriately explored even though modern advances in speech
technology—especially those driven by deep learning (DL) technology—offer …

The ultimate goal of transfer learning is to reduce labeled data requirements by exploiting a
pre-existing embedding model trained for different datasets or tasks. The visual and …

Nowadays speech synthesis or text to speech (TTS), an ability of system to produce human
like natural sounding voice from the written text, is gaining popularity in the field of speech …

Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …

Time–frequency representations of the speech signals provide dynamic information about
how the frequency component changes with time. In order to process this information, deep …