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Towards a Perceptual Model for Estimating the Quality of Visual Speech ...
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An error correction scheme for improved air-tissue boundary in real-time MRI video for speech production ...
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Expression-preserving face frontalization improves visually assisted speech processing ...
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Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language Modeling ...
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Facetron: Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations ...
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Silent Speech and Emotion Recognition from Vocal Tract Shape Dynamics in Real-Time MRI ...
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Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism ...
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Cascaded Multilingual Audio-Visual Learning from Videos ...
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Speaker embeddings by modeling channel-wise correlations ...
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Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation ...
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Abstract:
Decades of research has studied how language learning infants learn to discriminate speech sounds, segment words, and associate words with their meanings. While gradual development of such capabilities is unquestionable, the exact nature of these skills and the underlying mental representations yet remains unclear. In parallel, computational studies have shown that basic comprehension of speech can be achieved by statistical learning between speech and concurrent referentially ambiguous visual input. These models can operate without prior linguistic knowledge such as representations of linguistic units, and without learning mechanisms specifically targeted at such units. This has raised the question of to what extent knowledge of linguistic units, such as phone(me)s, syllables, and words, could actually emerge as latent representations supporting the translation between speech and representations in other modalities, and without the units being proximal learning targets for the learner. In this study, we ... : Final manuscript published in Language Development Research under CC BY-NC-SA 4.0. Pre-print redistributed through arXiv with permission. Replaces corrupted PsyArXiv pre-print repository at https://psyarxiv.com/37zna ...
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Keyword:
Artificial Intelligence cs.AI; Audio and Speech Processing eess.AS; Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; I.2.0; I.2.6; I.2.7; I.2.10; Machine Learning cs.LG; Sound cs.SD
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URL: https://arxiv.org/abs/2109.14200 https://dx.doi.org/10.48550/arxiv.2109.14200
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Cross-modal Speaker Verification and Recognition: A Multilingual Perspective ...
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Designing, Playing, and Performing with a Vision-based Mouth Interface ...
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SLNSpeech: solving extended speech separation problem by the help of sign language ...
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Unsupervised Audiovisual Synthesis via Exemplar Autoencoders ...
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UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation ...
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Disentangled Speech Embeddings using Cross-modal Self-supervision ...
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