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...
As Alzheimer's disease (AD) has increasingly become a major global public health issue, speech-based AD detection has attracted widespread attention. However, most existing methods are trained and evaluated on a single dataset, often leading to severe cross-domain performance degradation due to reliance on dataset-spec...
Lu Sun, Shreeram Suresh Chandra, Aurosweta Mahapatra et al.· 0 citations
Speech deepfake detection (SDD) systems achieve strong performance on conventional benchmarks; however, existing datasets provide limited coverage of emotionally expressive and recent large audio-language model (LALM)-based attacks. Existing emotional spoofing datasets are also limited in scale and attack diversity, ty...
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, S. Chandra, Lin Zhang et al.· 0 citations
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