Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance

• URL: http://arxiv.org/abs/2412.10417v1
• Date: Mon, 09 Dec 2024 20:40:03 GMT
• Title: Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance
• Authors: Abdelrahman A. Ali, Aya E. Fouda, Radwa J. Hanafy, Mohammed E. Fouda,
• Abstract summary: This study explores the potential of Large Language Models (LLMs) in multimodal mental health diagnostics.<n>We compare text and audio modalities to investigate whether LLMs can perform equally well or better with audio inputs.
• Score: 0.9074663948713616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mental health disorders are increasingly prevalent worldwide, creating an urgent need for innovative tools to support early diagnosis and intervention. This study explores the potential of Large Language Models (LLMs) in multimodal mental health diagnostics, specifically for detecting depression and Post Traumatic Stress Disorder through text and audio modalities. Using the E-DAIC dataset, we compare text and audio modalities to investigate whether LLMs can perform equally well or better with audio inputs. We further examine the integration of both modalities to determine if this can enhance diagnostic accuracy, which generally results in improved performance metrics. Our analysis specifically utilizes custom-formulated metrics; Modal Superiority Score and Disagreement Resolvement Score to evaluate how combined modalities influence model performance. The Gemini 1.5 Pro model achieves the highest scores in binary depression classification when using the combined modality, with an F1 score of 0.67 and a Balanced Accuracy (BA) of 77.4%, assessed across the full dataset. These results represent an increase of 3.1% over its performance with the text modality and 2.7% over the audio modality, highlighting the effectiveness of integrating modalities to enhance diagnostic accuracy. Notably, all results are obtained in zero-shot inferring, highlighting the robustness of the models without requiring task-specific fine-tuning. To explore the impact of different configurations on model performance, we conduct binary, severity, and multiclass tasks using both zero-shot and few-shot prompts, examining the effects of prompt variations on performance. The results reveal that models such as Gemini 1.5 Pro in text and audio modalities, and GPT-4o mini in the text modality, often surpass other models in balanced accuracy and F1 scores across multiple tasks.

Related papers

• Can Reasoning LLMs Enhance Clinical Document Classification? [7.026393789313748]
  Large Language Models (LLMs) offer promising improvements in accuracy and efficiency for this task. This study evaluates the performance and consistency of eight LLMs; four reasoning (Qwen QWQ, Deepseek Reasoner, GPT o3 Mini, Gemini 2.0 Flash Thinking) and four non-reasoning (Llama 3.3, GPT 4o Mini, Gemini 2.0 Flash, Deepseek Chat) Results showed that reasoning models outperformed non-reasoning models in accuracy (71% vs 68%) and F1 score (67% vs 60%)
  arXiv  Detail & Related papers  (2025-04-10T18:00:27Z)
• $C^2$AV-TSE: Context and Confidence-aware Audio Visual Target Speaker Extraction [80.57232374640911]
  We propose a model-agnostic strategy called the Mask-And-Recover (MAR) MAR integrates both inter- and intra-modality contextual correlations to enable global inference within extraction modules. To better target challenging parts within each sample, we introduce a Fine-grained Confidence Score (FCS) model.
  arXiv  Detail & Related papers  (2025-04-01T13:01:30Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references. We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey. Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• Dementia Insights: A Context-Based MultiModal Approach [0.3749861135832073]
  Early detection is crucial for timely interventions that may slow disease progression. Large pre-trained models (LPMs) for text and audio have shown promise in identifying cognitive impairments. This study proposes a context-based multimodal method, integrating both text and audio data using the best-performing LPMs.
  arXiv  Detail & Related papers  (2025-03-03T06:46:26Z)
• Audio Large Language Models Can Be Descriptive Speech Quality Evaluators [46.765203628127345]
  We introduce the first natural language-based speech evaluation corpus, generated from authentic human ratings. This corpus offers detailed analysis across multiple dimensions and identifies causes of quality degradation. We propose an alignment approach with LLM distillation (ALLD) to guide the audio LLM in extracting relevant information from raw speech.
  arXiv  Detail & Related papers  (2025-01-27T22:47:51Z)
• Baichuan-Omni-1.5 Technical Report [78.49101296394218]
  Baichuan- Omni-1.5 is an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities. We establish a comprehensive data cleaning and synthesis pipeline for multimodal data, obtaining about 500B high-quality data. Second, an audio-tokenizer has been designed to capture both semantic and acoustic information from audio, enabling seamless integration and enhanced compatibility with MLLM.
  arXiv  Detail & Related papers  (2025-01-26T02:19:03Z)
• Where are we in audio deepfake detection? A systematic analysis over generative and detection models [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark. It provides a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content. It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• Density Adaptive Attention-based Speech Network: Enhancing Feature Understanding for Mental Health Disorders [0.8437187555622164]
  We introduce DAAMAudioCNNLSTM and DAAMAudioTransformer, two parameter efficient and explainable models for audio feature extraction and depression detection. Both models' significant explainability and efficiency in leveraging speech signals for depression detection represent a leap towards more reliable, clinically useful diagnostic tools.
  arXiv  Detail & Related papers  (2024-08-31T08:50:28Z)
• Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques [0.0]
  Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk. Timely detection and prognostication of VAP in TBI patients are crucial to improve patient outcomes and alleviate the strain on healthcare resources. We implemented six machine learning models using the MIMIC-III database.
  arXiv  Detail & Related papers  (2024-08-02T09:44:18Z)
• Low-resource classification of mobility functioning information in clinical sentences using large language models [0.0]
  This study evaluates the ability of publicly available large language models (LLMs) to accurately identify the presence of functioning information from clinical notes. We collect a balanced binary classification dataset of 1000 sentences from the Mobility NER dataset, which was curated from n2c2 clinical notes.
  arXiv  Detail & Related papers  (2023-12-15T20:59:17Z)
• A Few-Shot Approach to Dysarthric Speech Intelligibility Level Classification Using Transformers [0.0]
  Dysarthria is a speech disorder that hinders communication due to difficulties in articulating words. Much of the literature focused on improving ASR systems for dysarthric speech. This work aims to develop models that can accurately classify the presence of dysarthria.
  arXiv  Detail & Related papers  (2023-09-17T17:23:41Z)
• Multimodal Imbalance-Aware Gradient Modulation for Weakly-supervised Audio-Visual Video Parsing [107.031903351176]
  Weakly-separated audio-visual video parsing (WS-AVVP) aims to localize the temporal extents of audio, visual and audio-visual event instances. WS-AVVP aims to identify the corresponding event categories with only video-level category labels for training.
  arXiv  Detail & Related papers  (2023-07-05T05:55:10Z)
• Deep Feature Learning for Medical Acoustics [78.56998585396421]
  The purpose of this paper is to compare different learnables in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbeats in two categories, i.e. healthy or affected by pathologies.
  arXiv  Detail & Related papers  (2022-08-05T10:39:37Z)
• On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition [53.17176024917725]
  Scarcity of speaker-level data limits the practical use of data-intensive model based speaker adaptation methods. This paper proposes two novel forms of data-efficient, feature-based on-the-fly speaker adaptation methods.
  arXiv  Detail & Related papers  (2022-03-28T09:12:24Z)
• On Modality Bias Recognition and Reduction [70.69194431713825]
  We study the modality bias problem in the context of multi-modal classification. We propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned. Our method yields remarkable performance improvements compared with the baselines.
  arXiv  Detail & Related papers  (2022-02-25T13:47:09Z)
• Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN [14.994852548758825]
  We propose an attention-based multimodality speech and text representation for depression prediction. Our model is trained to estimate the depression severity of participants using the Distress Analysis Interview Corpus-Wizard of Oz dataset. Experiments show statistically significant improvements over previous works.
  arXiv  Detail & Related papers  (2022-02-25T01:42:29Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning [49.41766997393417]
  The system we used for Task 6 (Automated Audio Captioning)of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines three elements, namely, dataaugmentation, multi-task learning, and post-processing, for audiocaptioning. The system received the highest evaluation scores, but which of the individual elements most fully contributed to its perfor-mance has not yet been clarified.
  arXiv  Detail & Related papers  (2020-09-24T01:07:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.