The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

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 ability of artificial intelligence (AI) systems to perceive and comprehend audio signals is
crucial for many applications. Although significant progress has been made in this area …

We investigate the emergent abilities of the recently proposed web-scale speech model
Whisper, by adapting it to unseen tasks with prompt engineering. We selected three tasks …

Recently, large pretrained language models have demonstrated strong language
understanding capabilities. This is particularly reflected in their zero-shot and in-context …

Prompt tuning is a technology that tunes a small set of parameters to steer a pre-trained
language model (LM) to directly generate the output for downstream tasks. Recently, prompt …

In this study, we aim to explore efficient tuning methods for speech self-supervised learning.
Recent studies show that self-supervised learning (SSL) can learn powerful representations …

Large language models (LLMs) have gained considerable attention for Artificial Intelligence
Generated Content (AIGC), particularly with the emergence of ChatGPT. However, the direct …

In this work, we propose a new parameter-efficient learning framework based on neural
model reprogramming for cross-lingual speech recognition, which can re-purpose well …

The past decade has witnessed substantial growth of data-driven speech enhancement (SE)
techniques thanks to deep learning. While existing approaches have shown impressive …