The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are
extensively being harnessed across a diverse range of domains, eg, forensic science …

Neural codecs have become crucial to recent speech and audio generation research. In
addition to signal compression capabilities, discrete codecs have also been found to …

Singing Voice Synthesis (SVS) has witnessed significant advancements with the advent of
deep learning techniques. However, a significant challenge in SVS is the scarcity of labeled …

This research presents Muskits-ESPnet, a versatile toolkit that introduces new paradigms to
Singing Voice Synthesis (SVS) through the application of pretrained audio models in both …

Speech discrete representation has proven effective in various downstream applications
due to its superior compression rate of the waveform, fast convergence during training, and …

Speech dysfluency modeling is the core module for spoken language learning, and speech
therapy. However, there are three challenges. First, current state-of-the-art solutions\cite …

Discrete representation has shown advantages in speech generation tasks, wherein
discrete tokens are derived by discretizing hidden features from self-supervised learning …

Recent singing voice synthesis and conversion advancements necessitate robust singing
voice deepfake detection (SVDD) models. Current SVDD datasets face challenges due to …

Recent advancements in speech synthesis witness significant benefits by leveraging
discrete tokens extracted from self-supervised learning (SSL) models. Discrete tokens offer …

The utilization of speech Self-Supervised Learning (SSL) models achieves impressive
performance on Automatic Speech Recognition (ASR). However, in low-resource language …