Speech enhancement and speech separation are two related tasks, whose purpose is to
extract either one or more target speech signals, respectively, from a mixture of sounds …

This paper presents an audio-visual approach for voice separation which produces state-of-
the-art results at a low latency in two scenarios: speech and singing voice. The model is …

Diffusion model, as a new generative model which is very popular in image generation and
audio synthesis, is rarely used in speech enhancement. In this paper, we use the diffusion …

We propose SE-Bridge, a novel method for speech enhancement (SE). After recently
applying the diffusion models to speech enhancement, we can achieve speech …

Existing research on audio–text retrieval is limited by the size of the dataset and the structure
of the network, making it difficult to learn the ideal features of audio and text resulting in low …

Deep latent variable generative models based on variational autoencoder (VAE) have
shown promising performance for audio-visual speech enhancement (AVSE). The …

The scarcity of driving data constrains the accuracy of deep learning (DL)-based driver
identification methods in practical application scenarios. To address this issue, this study …

Recently, audio-visual speech enhancement has been tackled in the unsupervised settings
based on variational auto-encoders (VAEs), where during training only clean data is used to …

Existing audio-visual cross-modal matching methods focus on mitigating cross-modal
heterogeneity but ignore the impact of intra-class discrepancy of the same identity in …

In this paper, we are interested in audio-visual speech separation given a single-channel
audio recording as well as visual information (lips movements) associated with each …