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1
Towards a Perceptual Model for Estimating the Quality of Visual Speech ...
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2
An error correction scheme for improved air-tissue boundary in real-time MRI video for speech production ...
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3
Expression-preserving face frontalization improves visually assisted speech processing ...
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4
Freeform Body Motion Generation from Speech ...
Xu, Jing; Zhang, Wei; Bai, Yalong. - : arXiv, 2022
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5
Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language Modeling ...
Peng, Puyuan; Harwath, David. - : arXiv, 2022
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6
Speaker Extraction with Co-Speech Gestures Cue ...
Pan, Zexu; Qian, Xinyuan; Li, Haizhou. - : arXiv, 2022
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7
Facetron: Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations ...
Um, Se-Yun; Kim, Jihyun; Lee, Jihyun. - : arXiv, 2021
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8
Silent Speech and Emotion Recognition from Vocal Tract Shape Dynamics in Real-Time MRI ...
Pandey, Laxmi; Arif, Ahmed Sabbir. - : arXiv, 2021
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9
Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism ...
Liu, Haiyang; Zhang, Jihan. - : arXiv, 2021
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10
Cascaded Multilingual Audio-Visual Learning from Videos ...
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11
Speaker embeddings by modeling channel-wise correlations ...
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12
Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation ...
Khorrami, Khazar; Räsänen, Okko. - : arXiv, 2021
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13
AudioViewer: Learning to Visualize Sounds ...
Abstract: A long-standing goal in the field of sensory substitution is enabling sound perception for deaf people by visualizing audio content. Different from existing models that translate between speech and text or text and images, we target immediate and low-level audio to video translation that applies to generic environment sounds as well as human speech. Since such a substitution is artificial, without labels for supervised learning, our core contribution is to build a mapping from audio to video that learns from unpaired examples via high-level constraints. For speech, we additionally disentangle content (phones) from style (gender and dialect) by mapping them to a common disentangled latent space. Qualitative and quantitative results, including a user study, demonstrate that our unpaired translation approach maintains important audio features in the generated video and that videos of faces and numbers are well suited for visualizing high-dimensional audio features that can be parsed by humans to match and ...
Keyword: Audio and Speech Processing eess.AS; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Human-Computer Interaction cs.HC; Machine Learning cs.LG; Sound cs.SD
URL: https://arxiv.org/abs/2012.13341
https://dx.doi.org/10.48550/arxiv.2012.13341
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14
Large-scale multilingual audio visual dubbing ...
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15
Cross-modal Speaker Verification and Recognition: A Multilingual Perspective ...
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16
Designing, Playing, and Performing with a Vision-based Mouth Interface ...
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17
SLNSpeech: solving extended speech separation problem by the help of sign language ...
Wu, Jiasong; Li, Taotao; Kong, Youyong. - : arXiv, 2020
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18
Unsupervised Audiovisual Synthesis via Exemplar Autoencoders ...
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19
UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation ...
Luo, Huaishao; Ji, Lei; Shi, Botian. - : arXiv, 2020
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20
Disentangled Speech Embeddings using Cross-modal Self-supervision ...
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