Xinmeng Xu

Xinmeng Xu, Haoran Xie, S. Joe Qin et al.

Stage-wise audio-visual encoders propagate fused intermediate states across layers, making the formation of later representations depend on the readiness of earlier fusion states. Strong local audio-visual agreement provides useful correspondence evidence, yet a fused state also needs sufficient cross-layer and cross-modal support before it can reliably guide later fusion. This paper studies this issue through propagation-aware representation readiness and formulates premature perceptual commitment as a readiness-deficiency problem, where local plausibility, propagation influence, and support insufficiency jointly appear at an intermediate stage. We propose the Delayed Perceptual Commitment Network (DPC-Net), an encoder-level framework that estimates an observable readiness-deficiency surrogate, localizes the intervention-sensitive bottleneck, and applies support-aware correction with cross-layer and cross-modal evidence. DPC-Net preserves task-specific heads, losses, decoding modules, and evaluation protocols, making it applicable to different audio-visual tasks through encoder-side intervention. Experiments on audio-visual speech separation, audio-visual event localization, and audio-visual speech recognition show consistent improvements across reconstruction, localization, and recognition regimes. Further analyses on component contribution, selection criteria, counterfactual intervention, and readiness trajectories support the effectiveness of readiness-guided bottleneck correction.

Xinmeng Xu, Yang Wang, Dongxiang Xu et al.

Speech enhancement is an essential task of improving speech quality in noise scenario. Several state-of-the-art approaches have introduced visual information for speech enhancement,since the visual aspect of speech is essentially unaffected by acoustic environment. This paper proposes a novel frameworkthat involves visual information for speech enhancement, by in-corporating a Generative Adversarial Network (GAN). In par-ticular, the proposed visual speech enhancement GAN consistof two networks trained in adversarial manner, i) a generator that adopts multi-layer feature fusion convolution network to enhance input noisy speech, and ii) a discriminator that attemptsto minimize the discrepancy between the distributions of the clean speech signal and enhanced speech signal. Experiment re-sults demonstrated superior performance of the proposed modelagainst several state-of-the-art

Xinmeng Xu, Jianjun Hao

Audio-visual speech enhancement system is regarded to be one of promising solutions for isolating and enhancing speech of desired speaker. Conventional methods focus on predicting clean speech spectrum via a naive convolution neural network based encoder-decoder architecture, and these methods a) not adequate to use data fully and effectively, b) cannot process features selectively. The proposed model addresses these drawbacks, by a) applying a model that fuses audio and visual features layer by layer in encoding phase, and that feeds fused audio-visual features to each corresponding decoder layer, and more importantly, b) introducing soft threshold attention into the model to select the informative modality softly. This paper proposes attentional audio-visual multi-layer feature fusion model, in which soft threshold attention unit are applied on feature mapping at every layer of decoder. The proposed model demonstrates the superior performance of the network against the state-of-the-art models.

Xinmeng Xu, Jianjun Hao

Speech enhancement can potentially benefit from the visual information from the target speaker, such as lip movement and facial expressions, because the visual aspect of speech is essentially unaffected by acoustic environment. In this paper, we address the problem of enhancing corrupted speech signal from videos by using audio-visual (AV) neural processing. Most of recent AV speech enhancement approaches separately process the acoustic and visual features and fuse them via a simple concatenation operation. Although this strategy is convenient and easy to implement, it comes with two major drawbacks: 1) evidence in speech perception suggests that in humans the AV integration occurs at a very early stage, in contrast to previous models that process the two modalities separately at early stage and combine them only at a later stage, thus making the system less robust, and 2) a simple concatenation does not allow to control how the information from the acoustic and the visual modalities is treated. To overcome these drawbacks, we propose a multi-layer feature fusion convolution network (MFFCN), which separately process acoustic and visual modalities for preserving each modality features while fusing both modalities' features layer by layer in encoding phase for enjoying the human AV speech perception. In addition, considering the balance between the two modalities, we design channel and spectral attention mechanisms to provide additional flexibility in dealing with different types of information expanding the representational ability of the convolution neural network. Experimental results show that the proposed MFFCN demonstrates the performance of the network superior to the state-of-the-art models.