Speech enhancement (SE) is a critical aspect of various speech-processing applications.
Recent research in this field focuses on identifying effective ways to capture the long-term …

Prior studies on audio-visual speech recognition typically assume the visibility of speaking
lips, ignoring the fact that visual occlusion occurs in real-world videos, thus adversely …

Convolutional encoder-decoder (CED) has emerged as a powerful architecture, particularly
in speech enhancement (SE), which aims to improve the intelligibility and quality and …

Transformer architecture has enabled recent progress in speech enhancement. Since
Transformers are position-agostic, positional encoding is the de facto standard component …

Speech enhancement is an important method for improving speech quality and intelligibility
in noisy environments. An effective speech enhancement model depends on precise …

Most research in synthetic speech detection (SSD) focuses on improving performance on
standard noise-free datasets. However, in actual situations, noise interference is usually …

The use of Transformer represents a recent success in speech enhancement. However, as
its core component, self-attention suffers from quadratic complexity, which is computationally …

Transformer and its derivatives have achieved success in diverse tasks across computer
vision, natural language processing, and speech processing. To reduce the complexity of …

Neural network approaches to single-channel speech enhancement have received much
recent attention. In particular, mask-based architectures have achieved significant …

In many speech separation methods, the contextual information contained in the feature
sequence is mainly modeled by recurrent layer and/or self-attention mechanism. However …