Public articles linked to the same research event.
arXiv Using ADReSSo and three large self-supervised speech backbones, the study applies controlled noise and reverberation interventions to participant-speech-only, non-speech, and full-recording audio, and combines layer-wise linear decoding, input- and representation-space interventions, and geometric alignment analysis to show that controlled acoustic interventions alter Alzheimer's disease predictions across all three backbones, that noise, despite showing no significant diagnostic-group difference in the original data, produces the strongest intervention effects, and that these effects are systematically structured relative to the classifier's decision direction, replicate on the held-out test set, and reverse when the representation-space intervention direction is reversed.
Using ADReSSo and three large self-supervised speech backbones, the study applies controlled noise and reverberation interventions to participant-speech-only, non-speech, and full-recording audio, and combines layer-wise linear decoding, input- and representation-space interventions, and geometric alignment analysis to show that controlled acoustic interventions alter Alzheimer's disease predictions across all three backbones, that noise, despite showing no significant diagnostic-group difference in the original data, produces the strongest intervention effects, and that these effects are systematically structured relative to the classifier's decision direction, replicate on the held-out test set, and reverse when the representation-space intervention direction is reversed.
Using ADReSSo and three large self-supervised speech backbones, the study applies controlled noise and reverberation interventions to participant-speech-only, non-speech, and full-recording audio, and combines layer-wise linear decoding, input- and representation-space interventions, and geometric alignment analysis to show that controlled acoustic interventions alter Alzheimer's disease predictions across all three backbones, that noise, despite showing no significant diagnostic-group difference in the original data, produces the strongest intervention effects, and that these effects are systematically structured relative to the classifier's decision direction, replicate on the held-out test set, and reverse when the representation-space intervention direction is reversed.
Using ADReSSo and three large self-supervised speech backbones, the study applies controlled noise and reverberation interventions to participant-speech-only, non-speech, and full-recording audio, and combines layer-wise linear decoding, input- and representation-space interventions, and geometric alignment analysis to show that controlled acoustic interventions alter Alzheimer's disease predictions across all three backbones, that noise, despite showing no significant diagnostic-group difference in the original data, produces the strongest intervention effects, and that these effects are systematically structured relative to the classifier's decision direction, replicate on the held-out test set, and reverse when the representation-space intervention direction is reversed.