DAIC-WoZ
PulseAugur coverage of DAIC-WoZ — every cluster mentioning DAIC-WoZ across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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New model DiaWhisper-DPO improves clinical interview transcription and role attribution
Researchers have developed DiaWhisper-DPO, an end-to-end model for transcribing clinical interviews and attributing utterances to either the clinician or patient. This model fine-tunes Whisper-large-v3 using LoRA and an…
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New AI framework estimates depression severity with uncertainty quantification
Researchers have developed EviDep, a novel framework for estimating depression severity using audio-visual data. This system employs evidential learning to quantify both aleatoric and epistemic uncertainty in its predic…
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New framework links speech acoustics to depression indicators
Researchers have developed a novel framework for detecting depression by analyzing speech acoustics and linking them to specific DSM-5 indicators. This approach aims to provide more objective and interpretable diagnosti…
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New AI framework enhances explainable depression recognition in clinical interviews
Researchers have developed Explain-MDRC, a novel framework for multimodal depression recognition in clinical interviews. This system aims to enhance interpretability by generating structured symptom summaries from text …
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AI framework enhances mental health supervision and risk triage
Researchers have developed a novel AI framework designed to assist in mental healthcare by providing automated clinical supervision and risk triage. This system utilizes a fine-tuned Mistral-7B-instruct model to analyze…
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New AI Model Predicts Depression Severity with Uncertainty Estimates
Researchers have developed PTTSD, a novel probabilistic framework designed to detect depression severity from clinical interview transcripts. This system utilizes LSTMs and self-attention mechanisms to predict PHQ-8 sco…
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New MA-DLE method estimates depression levels from speech
Researchers have developed a new method called MA-DLE for estimating depression levels using speech analysis. This approach augments standard GRU-extracted features with a memory bank that selectively integrates histori…
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Dep-LLM uses LLMs for training-free depression diagnosis
Researchers have developed Dep-LLM, a novel framework for diagnosing depression from clinical interviews without requiring any additional training. This system leverages existing large language models (LLMs) by mimickin…
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Speech analysis framework aids mental health clinical decisions
Researchers have developed a framework for analyzing speech features to aid in clinical decision-making for mental health care. This system uses perceptually grounded acoustic and linguistic characteristics, such as pro…
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New EmoTrack framework improves depression tracking from counseling transcripts
Researchers have developed EmoTrack, a new framework designed to more accurately track depression severity from counseling transcripts. This system combines signals extracted by large language models with semantic embed…
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AI tools enhance campus well-being via chatbots and mental health detection
Researchers have developed AI tools to improve campus well-being by enhancing feedback collection and mental health detection. TigerGPT, a chatbot, uses LLMs for personalized surveys, achieving high usability and satisf…
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New PsyGAT model achieves SOTA in depression detection, outperforming GPT-5
Researchers have developed PsyGAT, a novel graph-based framework for detecting depression from conversational data. This model addresses data scarcity and interpretability issues common in existing deep learning approac…