The loss of speech limits communication for individuals with paralysis. Direct neural-to-speech synthesis is challenging due to the limited availability of neural data for training speech brain-computer interfaces. Most existing systems rely on cascaded neural-to-text-to-speech pipelines, which increase inference laten...
Audio-visual speech enhancement (AVSE) uses a target speaker's visible articulation to recover that speaker's voice from overlapped speech. Yet most evaluation protocols rely on synthetic mixtures and reliable video. The ISCSLP 2026 Real-World AVSE Challenge addresses both gaps. Track~1 combines naturally recorded two-...
Kai Li, Wen-Ze Ren, Jun-Jie Li et al.· 0 citations
Brain2Speech-Net is presented, among the first single-stage frameworks to remain intelligible under limited data while removing intermediate text decoding, and achieves strong intelligibility in objective and listening tests while running faster than real time.
The role of speech preprocessing and dataset curation across widely used benchmarks for speech-based AD detection is revisited, suggesting that ``cleaner''speech datasets are not necessarily more reliable for real-world AD detection.
Lu Sun, Shreeram Suresh Chandra, Lin Zhang et al.· 0 citations
TurnBench, a multi-domain benchmark that pairs a 30-hour, hand-labeled corpus of dyadic human conversation with a standardized evaluation protocol for end-of-turn and interruption detection, finds end-of-turn recall stable across types, while interruption false positives are strongly type-dependent and concentrated in...
Freeman Jiang, Ramon Sanabria, Soham Deshmukh et al.· 9 citations· ⚡4
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
