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Found 5 papers, 1 papers with code
Zero-Shot Multi-Lingual Speaker Verification in Clinical Trials
This represents a significant step in developing more versatile and efficient speaker verification systems for cognitive and mental health clinical trials that can be used across a wide range of languages and dialects, substantially reducing the effort required to develop speaker verification systems for multiple languages.
Arcee's MergeKit: A Toolkit for Merging Large Language Models
The rapid expansion of the open-source language model landscape presents an opportunity to merge the competencies of these model checkpoints by combining their parameters.
Factors Affecting the Performance of Automated Speaker Verification in Alzheimer's Disease Clinical Trials
Detecting duplicate patient participation in clinical trials is a major challenge because repeated patients can undermine the credibility and accuracy of the trial's findings and result in significant health and financial risks.
DEPAC: a Corpus for Depression and Anxiety Detection from Speech
Automated diagnosis systems of such disorders, empowered by recent innovations in Artificial Intelligence, can pave the way to reduce the sufferings of the affected individuals.
Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data
In this work, we investigate the ability of clustering approaches in distinguishing between depression and dementia from human speech.
