Visemebased

Viseme-based refers to an approach in speech processing and computer vision that models spoken language using visemes, the visual counterparts of phonemes observable on the lips, teeth, and tongue. The term is most often encountered in lip-reading, audiovisual speech recognition, and visual speech synthesis. Because there is no universal standard viseme set, inventories typically include about 10 to 15 visual categories, and mappings from phonemes to visemes vary by language and system.

Viseme-based systems rely on the fact that different phonemes may look the same or very similar on the face, an issue known as homopheny. As a result, recognition is framed as distinguishing a smaller, visually separable set of classes rather than the full phoneme inventory. Visual signals are often combined with audio signals in audiovisual systems to improve accuracy, while some applications focus on silent speech interfaces that infer visemes from video alone.

In practice, viseme-based pipelines detect a mouth region, extract spatiotemporal features (for example via CNNs, CNN-LSTMs, or transformer-based models), and classify segments into viseme categories. Advanced systems may align viseme sequences with phonetic or linguistic information or use end-to-end learning to map video to spoken content. Training relies on labeled datasets that pair video with phoneme or viseme annotations, and evaluation considers viseme recognition accuracy and downstream word-level performance.

Applications include silent communication tools, accessibility technology for the deaf and hard of hearing, lip-sync for animation and dubbing, and improvements in audiovisual speech recognition. Challenges remain in handling speaker variability, lighting, occlusions, coarticulation, and cross-language viseme mappings.

Common datasets used in viseme and lip-reading research include GRID, Lip Reading in the Wild (LRW), and LRS2/LRS3, among others.