Cooperative Dual Attention for Audio-Visual Speech Enhancement with Facial Cues

• URL: http://arxiv.org/abs/2311.14275v1
• Date: Fri, 24 Nov 2023 04:30:31 GMT
• Title: Cooperative Dual Attention for Audio-Visual Speech Enhancement with Facial Cues
• Authors: Feixiang Wang, Shuang Yang, Shiguang Shan, Xilin Chen
• Abstract summary: We focus on leveraging facial cues beyond the lip region for robust Audio-Visual Speech Enhancement (AVSE) We propose a Dual Attention Cooperative Framework, DualAVSE, to ignore speech-unrelated information, capture speech-related information with facial cues, and dynamically integrate it with the audio signal for AVSE.
• Score: 80.53407593586411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we focus on leveraging facial cues beyond the lip region for robust Audio-Visual Speech Enhancement (AVSE). The facial region, encompassing the lip region, reflects additional speech-related attributes such as gender, skin color, nationality, etc., which contribute to the effectiveness of AVSE. However, static and dynamic speech-unrelated attributes also exist, causing appearance changes during speech. To address these challenges, we propose a Dual Attention Cooperative Framework, DualAVSE, to ignore speech-unrelated information, capture speech-related information with facial cues, and dynamically integrate it with the audio signal for AVSE. Specifically, we introduce a spatial attention-based visual encoder to capture and enhance visual speech information beyond the lip region, incorporating global facial context and automatically ignoring speech-unrelated information for robust visual feature extraction. Additionally, a dynamic visual feature fusion strategy is introduced by integrating a temporal-dimensional self-attention module, enabling the model to robustly handle facial variations. The acoustic noise in the speaking process is variable, impacting audio quality. Therefore, a dynamic fusion strategy for both audio and visual features is introduced to address this issue. By integrating cooperative dual attention in the visual encoder and audio-visual fusion strategy, our model effectively extracts beneficial speech information from both audio and visual cues for AVSE. Thorough analysis and comparison on different datasets, including normal and challenging cases with unreliable or absent visual information, consistently show our model outperforming existing methods across multiple metrics.

Related papers

• Robust Audiovisual Speech Recognition Models with Mixture-of-Experts [67.75334989582709]
  We introduce EVA, leveraging the mixture-of-Experts for audioVisual ASR to perform robust speech recognition for in-the-wild'' videos. We first encode visual information into visual tokens sequence and map them into speech space by a lightweight projection. Experiments show our model achieves state-of-the-art results on three benchmarks.
  arXiv  Detail & Related papers  (2024-09-19T00:08:28Z)
• Learning Video Temporal Dynamics with Cross-Modal Attention for Robust Audio-Visual Speech Recognition [29.414663568089292]
  Audio-visual speech recognition aims to transcribe human speech using both audio and video modalities. In this study, we strengthen the video features by learning three temporal dynamics in video data. We achieve the state-of-the-art performance on the LRS2 and LRS3 AVSR benchmarks for the noise-dominant settings.
  arXiv  Detail & Related papers  (2024-07-04T01:25:20Z)
• Bootstrapping Audio-Visual Segmentation by Strengthening Audio Cues [75.73217916395386]
  We propose a Bidirectional Audio-Visual Decoder (BAVD) with integrated bidirectional bridges. This interaction narrows the modality imbalance, facilitating more effective learning of integrated audio-visual representations. We also present a strategy for audio-visual frame-wise synchrony as fine-grained guidance of BAVD.
  arXiv  Detail & Related papers  (2024-02-04T03:02:35Z)
• Speech inpainting: Context-based speech synthesis guided by video [29.233167442719676]
  This paper focuses on the problem of audio-visual speech inpainting, which is the task of synthesizing the speech in a corrupted audio segment. We present an audio-visual transformer-based deep learning model that leverages visual cues that provide information about the content of the corrupted audio. We also show how visual features extracted with AV-HuBERT, a large audio-visual transformer for speech recognition, are suitable for synthesizing speech.
  arXiv  Detail & Related papers  (2023-06-01T09:40:47Z)
• AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR [79.21857972093332]
  We present AVFormer, a method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively.
  arXiv  Detail & Related papers  (2023-03-29T07:24:28Z)
• Audio-Visual Activity Guided Cross-Modal Identity Association for Active Speaker Detection [37.28070242751129]
  Active speaker detection in videos addresses associating a source face, visible in the video frames, with the underlying speech in the audio modality. We propose a novel unsupervised framework to guide the speakers' cross-modal identity association with the audio-visual activity for active speaker detection.
  arXiv  Detail & Related papers  (2022-12-01T14:46:00Z)
• Egocentric Audio-Visual Noise Suppression [11.113020254726292]
  This paper studies audio-visual noise suppression for egocentric videos. Video camera emulates off-screen speaker's view of the outside world. We first demonstrate that egocentric visual information is helpful for noise suppression.
  arXiv  Detail & Related papers  (2022-11-07T15:53:12Z)
• AVATAR: Unconstrained Audiovisual Speech Recognition [75.17253531162608]
  We propose a new sequence-to-sequence AudioVisual ASR TrAnsformeR (AVATAR) trained end-to-end from spectrograms and full-frame RGB. We demonstrate the contribution of the visual modality on the How2 AV-ASR benchmark, especially in the presence of simulated noise. We also create a new, real-world test bed for AV-ASR called VisSpeech, which demonstrates the contribution of the visual modality under challenging audio conditions.
  arXiv  Detail & Related papers  (2022-06-15T17:33:19Z)
• Joint Learning of Visual-Audio Saliency Prediction and Sound Source Localization on Multi-face Videos [101.83513408195692]
  We propose a multitask learning method for visual-audio saliency prediction and sound source localization on multi-face video. The proposed method outperforms 12 state-of-the-art saliency prediction methods, and achieves competitive results in sound source localization.
  arXiv  Detail & Related papers  (2021-11-05T14:35:08Z)
• Bio-Inspired Audio-Visual Cues Integration for Visual Attention Prediction [15.679379904130908]
  Visual Attention Prediction (VAP) methods simulates the human selective attention mechanism to perceive the scene. A bio-inspired audio-visual cues integration method is proposed for the VAP task, which explores the audio modality to better predict the visual attention map. Experiments are conducted on six challenging audiovisual eye-tracking datasets, including DIEM, AVAD, Coutrot1, Coutrot2, SumMe, and ETMD.
  arXiv  Detail & Related papers  (2021-09-17T06:49:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.