We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

Most existing datasets for sound event recognition (SER) are relatively small and/or domain-
specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and …

Although Deep Neural Networks (DNNs) have shown a strong capacity to solve large-scale
problems in many areas, such DNNs with voluminous parameters are hard to be deployed …

The performance of music source separation (MSS) models has been greatly improved in
recent years thanks to the development of novel neural network architectures and training …

Abstract Knowledge distillation extracts general knowledge from a pretrained teacher
network and provides guidance to a target student network. Most studies manually tie …

As GAN-based video and image manipulation technologies become more sophisticated and
easily accessible, there is an urgent need for effective deepfake detection technologies …

In this paper, we summarize recent progresses made in deep learning based acoustic
models and the motivation and insights behind the surveyed techniques. We first discuss …

With the broad industrialization of Artificial Intelligence (AI), we observe a large fraction of
real-world AI applications are multimodal in nature in terms of relevant data and ways of …

The goal of self-supervised learning (SSL) for automatic speech recognition (ASR) is to
learn good speech representations from a large amount of unlabeled speech for the …

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-
world settings such as negotiation, medical diagnosis, and criminal investigation. It serves …