7 articles tagged with #speech-analysis. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce LinguAS, a dataset of 800+ audio samples annotated with linguistic features to improve detection of deepfaked and spoofed speech. Models trained on this linguistically-augmented data significantly outperform existing deepfake detection baselines, addressing a critical gap in audio forensics.
Researchers have developed a deep learning model trained on ~65,000 speech samples from over 23,000 U.S. subjects that can detect depression and anxiety from voice biomarkers with 71% accuracy in sensitivity and specificity. The model extracts content-agnostic acoustic features combined with lexical information, demonstrating that raw speech analysis outperforms traditional hand-engineered acoustic descriptors for mental health screening.
Researchers introduce MA-DLE, a deep learning method that uses memory augmentation and attention mechanisms to improve speech-based depression level estimation. The approach selectively integrates historical temporal features and dynamic memory components to better capture long-range dependencies in speech patterns, achieving state-of-the-art results on standard datasets.
Researchers developed a semantic-timescale analysis pipeline to compare how human and AI-generated speech organize semantic content over time. Using autocorrelation measures on word specificity and contextual similarity, they found that temporal clustering of generic versus specific vocabulary distinguishes human narratives from LLM outputs, revealing non-trivial structural differences beyond static word frequency.
Researchers introduce InfoShield, a privacy-preserving machine learning technique that maintains depression detection accuracy while preventing the inference of sensitive demographic attributes from speech data. The method uses information-theoretic optimization to reduce mutual information between speech representations and demographic information, addressing a critical barrier to clinical deployment of speech-based mental health screening.
Researchers have developed a speech analysis framework that uses acoustic and linguistic features to support mental health assessment for depression, anxiety, and ADHD. The approach combines interpretable machine learning with clinically grounded speech markers like prosody and vocal quality, demonstrating consistent relationships between speech patterns and symptom severity across multiple datasets.
Researchers developed MINT, a framework that transfers knowledge from MRI brain scans to speech analysis for early Alzheimer's detection. The system achieves comparable performance to speech-only methods while being grounded in neuroimaging biomarkers, enabling population-scale screening without requiring expensive MRI scans at inference.
