Automatic Health Assessment from Voice

• Automatic health assessment from voice is a multifaceted field that applies audio signal processing, machine learning, and multimodal data fusion to diagnose health conditions.
• Key methodologies involve rigorous data preprocessing, advanced noise reduction, and extraction of classical and deep acoustic features like MFCC and learned embeddings.
• Practical applications span mental health, respiratory illnesses, neurological disorders, and vocal pathologies, with high accuracy and interpretability validated through clinical studies.

Automatic health assessment from voice refers to the use of audio recordings—primarily of speech, but also including structured vocal and respiratory sounds—to evaluate physical, mental, or neurological health, either as a primary screening tool or in support of clinical workflows. Drawing on a spectrum of signal processing, machine learning, and multimodal fusion approaches, current research demonstrates the feasibility, performance, and remaining challenges of automatic voice-based health diagnostics across a range of use cases, from mental health and well-being to respiratory illness, neurodegeneration, and vocal pathology.

1. Data Acquisition, Preprocessing, and Multimodal Context

The first stage of automatic health assessment from voice is robust data collection and preparation. Systems rely on varied data sources, including scripted and spontaneous speech captured in clinics, telemedicine, home care environments, or via mobile/web applications (Anibal et al., 2 Apr 2024, Chen et al., 20 Oct 2025).

Preprocessing pipelines typically address:

• Noise reduction and enhancement: Real-time hardware filtering (e.g., on embedded devices (Yvanoff-Frenchin et al., 2019)), digital denoising, and manual removal of silences to ensure signal fidelity—even in noisy or low-quality telephonic data (Rashid et al., 2020, Chen et al., 20 Oct 2025).
• Segmentation and diarization: Automatic or manual speaker separation and selection, frame-wise windowing (commonly 20–40 ms windows for spectral analysis), and detection of target speech periods (e.g., 4 s fixed-length segments (Yvanoff-Frenchin et al., 2019, Levinson et al., 2023)).
• Normalization and alignment: Amplitude normalization and temporal/spatial feature alignment to address cross-device and cross-session variability.

Data collection strategies emphasize multimodality:

• Multimodal prompts and context: Structured acoustic tasks (sustained vowels, sentence or paragraph reading), spontaneous health narratives, and breathing exercises for richer biomarker extraction (Anibal et al., 2 Apr 2024).
• Survey/demographic linkage: Metadata, self-reported health questionnaires, and parallel text or symptom reports are integrated for improved feature richness or ground-truth labeling (Kim et al., 2019, Chen et al., 20 Oct 2025).

2. Signal Representation, Feature Extraction, and Acoustic Biomarkers

Feature engineering combines classical low-level descriptors with high-dimensional learned representations:

• Classical features: Mel-frequency cepstral coefficients (MFCC), Perceptual Linear Prediction (PLP) coefficients, pitch, jitter, shimmer, HNR, voice/pause durations, zero-crossing rate, and prosodic measures (Kim et al., 2019, Lin et al., 2023, Rashid et al., 2020, Ariyanti et al., 27 May 2025, Siva et al., 11 Aug 2025).
• Statistical functionals: Means, medians, skewness, kurtosis, quantiles over frame-level features; handcrafted summary statistics for session- or utterance-level aggregation (Kim et al., 2019, Rashid et al., 2020).
• Scale-based features: Hölder and Hurst exponents quantify local irregularities and long-term speech dynamics, providing sensitivity to phasic and chronic disruptions (Siva et al., 11 Aug 2025).
• Learned and foundation model embeddings: Deep audio representations are extracted from pretrained speech foundation models (e.g., Whisper, WavLM, Audio Spectrogram Transformer), with layer-wise attention and/or weighted aggregation to incorporate multi-level temporal/spectral information (Ariyanti et al., 27 May 2025, Lau et al., 29 Jun 2024).

Modern systems increasingly employ joint embeddings of acoustic and semantic content—aligning transcribed speech and raw audio in shared latent spaces (Anibal et al., 2 Apr 2024, Qin et al., 22 Aug 2024).

3. Machine Learning Frameworks and Modeling Strategies

Automatic health assessment from voice leverages a broad spectrum of machine learning and deep learning methodologies:

• Classical algorithms: Support Vector Machines (Han et al., 2020, Dhakal et al., 2020), k-Nearest Neighbors (Levinson et al., 2023), and Random Forests (Lin et al., 2023) are effective for lower-dimensional, interpretable features, particularly where data is limited.
• Fully-connected or recurrent architectures: Dense neural networks (4-layer FC-DNNs with ReLU) (Kim et al., 2019), RNNs and LSTMs (with or without attention) (Siva et al., 11 Aug 2025).
• Transformer-based and Mixture-of-Expert (MoE) models: Pretrained ASTs, MoE Transformers (VoiceMoETransformer), and frameworks with integrated attention mechanisms or expert routing/gating (Lau et al., 29 Jun 2024, Togootogtokh et al., 5 Mar 2025).
• Multitask and multimodal fusion: Dual-branch networks combining MFCC and spectrogram pathways with task-specific heads (as in MARVEL) enable effective knowledge transfer across disorders (Piao et al., 28 Aug 2025). Fusion of audio and transcribed text via attention-based schemes (e.g., Mental-Perceiver) consistently outperforms unimodal models (Qin et al., 22 Aug 2024).
• Foundation and LLMs (LLMs/ALMs): Audio LLMs, such as VocalAgent (Qwen-Audio-Chat) (Kim et al., 19 May 2025), and combination LLM/ALM agents for illness scoring and vocal biomarker interpretation (Chen et al., 20 Oct 2025), have advanced the field by supporting both classification and rationalized, clinician-interpretable outputs.

Training strategies may incorporate reinforcement learning paradigms (PPO, GRPO) for stable optimization, variance reduction, and efficient expert utilization (Togootogtokh et al., 5 Mar 2025). Synthetic oversampling (SMOTE) and rigorous cross-validation (LOSO, k-fold) are routinely applied (Dhakal et al., 2020, Han et al., 2021).

4. Application Domains and Clinical Validation

Voice-based health assessment is deployed across a range of domains:

• Mental and cognitive health: Automated assessment of depression, anxiety, sleep quality, and mood using rich acoustic/linguistic features and externally validated scales (e.g., GAD7, PSQI, PANAS) (Kim et al., 2019, Qin et al., 22 Aug 2024, Levinson et al., 2023).
• Respiratory illness: Classification of respiratory distress, severity of infection (notably COVID-19), sleep quality, fatigue, and anxiety from controlled clinical or telemedicine recordings (Han et al., 2020, Rashid et al., 2020).
• Neurological disorders: Detection and longitudinal tracking of Alzheimer’s disease and mild cognitive impairment using multiplexed, orthogonal biomarkers and explainable saliency mapping (Soler et al., 2021, Piao et al., 28 Aug 2025).
• Voice disorders and pathology: Detection and grading of dysphonia, nodules, and vocal fold lesions using robust acoustic features and regression frameworks for CAPE-V and GRBAS scoring (Lin et al., 2023, Ariyanti et al., 27 May 2025, Siva et al., 11 Aug 2025).
• Global screening and telehealth: Multilingual, scalable systems (e.g., voice EHR, HEAR app, Agent PULSE) for high-volume, low-resource, and home healthcare contexts (Anibal et al., 2 Apr 2024, Wen et al., 22 Jul 2025, Chen et al., 20 Oct 2025).

Clinical validation is consistently prioritized via cross-referenced outcomes—either with gold-standard clinical tests, expert rater scales, or subsequent health events (e.g., ED/hospitalization (Chen et al., 20 Oct 2025), self-assessment questionnaires (Kim et al., 2019)).

5. Evaluation Metrics, Performance, and Interpretability

Performance is assessed according to:

• Correlation/regression metrics: Concordance Correlation Coefficient (CCC), Pearson (PCC), RMSE—for continuous outcome prediction (e.g., well-being, perceptual scores) (Kim et al., 2019, Ariyanti et al., 27 May 2025).
• Classification metrics: Accuracy, F1, sensitivity, specificity, AUROC, and UAR—particularly in multi-class or multi-label detection scenarios (Rashid et al., 2020, Piao et al., 28 Aug 2025, Kim et al., 19 May 2025).
• Model interpretability: Attention rollout methods produce relevance maps linking spectrogram regions to predictions, facilitating clinical insight into model decisions and phoneme-level sensitivity (Lau et al., 29 Jun 2024). LLM and ALM agents generate natural language rationales (illness scores with justifications and plain-language biomarker descriptions) (Chen et al., 20 Oct 2025).

Notably, recent state-of-the-art models achieve high patient-level accuracy (e.g., OVBM: 93.8% in Alzheimer’s detection (Soler et al., 2021); GRPO-MoE: 0.9860 test accuracy on synthetic vocal pathology (Togootogtokh et al., 5 Mar 2025); MARVEL: AUROC 0.97 for Alzheimer’s/MCI (Piao et al., 28 Aug 2025); VocalAgent: macro-F1 >89 on AVFAD clinical data (Kim et al., 19 May 2025)), with robust performance across noise and recording variabilities, supporting real-world deployment.

6. Technical, Ethical, and Operational Considerations

Critical technical and ethical challenges include:

• Noise and device variability: Robustness to diverse recording conditions and channels, including telemedicine, home environments, and low-resource clinics, is paramount (Rashid et al., 2020, Anibal et al., 2 Apr 2024).
• Multilingual/cross-lingual adaptation: Many recent frameworks support multilingual inputs (dynamic translation, language-aware prompting), with performance varying by language and requiring language-specific validation (Yvanoff-Frenchin et al., 2019, Kim et al., 19 May 2025).
• Privacy and data security: Several frameworks (e.g., MARVEL) process only derived acoustic features (MFCC, spectrograms), not raw audio, minimizing risks to personally identifiable information (Piao et al., 28 Aug 2025). Emphasis is also placed on data encryption, anonymization, and regulatory compliance (HIPAA/GDPR) (Wen et al., 22 Jul 2025).
• Interpretability and trust: Explainability is addressed through modular architectures (human-readable rationales, saliency maps, and category priors), essential to clinician engagement and regulatory approval (Soler et al., 2021, Lau et al., 29 Jun 2024, Chen et al., 20 Oct 2025).
• Bias and evaluation: Safety-aware evaluations (jailbreak resistance, diagnostic bias tracking, misclassification risk, and overrefusal rates) are now incorporated in LLM-based diagnostic systems (Kim et al., 19 May 2025).
• Resource constraints: Voice-first and hybrid EHR systems (HEAR app, voice EHR) demonstrate low-bandwidth, scalable solutions for underserved regions (Anibal et al., 2 Apr 2024, Wen et al., 22 Jul 2025).

7. Future Directions

Active research areas and future priorities are:

• Unified and multitask models: Frameworks such as MARVEL demonstrate the ability to simultaneously detect multiple disorders, enabling more efficient screening and supporting cross-condition knowledge transfer (Piao et al., 28 Aug 2025).
• Advances in multi-modal and foundation models: Joint acoustic-textual embedding (as in Mental-Perceiver (Qin et al., 22 Aug 2024), voice EHR (Anibal et al., 2 Apr 2024)) augments predictive power and enables richer, more actionable predictions across varying patient populations.
• Longitudinal monitoring and personalized health trajectories: Fine-grained, session-by-session tracking supports proactive care, intervention efficacy measurement, and adaptive care planning (Soler et al., 2021, Chen et al., 20 Oct 2025).
• Integration with health systems: Progress is being made towards real-time, telephonic/edge deployment, APIs for EHR system integration, smart agent frameworks (Agent PULSE), and conversational AI interfaces with KV cache/state management optimization (Wen et al., 22 Jul 2025).
• Ethical and regulatory alignment: Ongoing efforts include implementing differential privacy, robust bias mitigation, and transparent model governance to meet evolving clinical and legal standards (Kim et al., 19 May 2025, Wen et al., 22 Jul 2025).
• Dataset expansion and equity: Initiatives focus on the democratization of data collection across broader populations to address previously underrepresented groups, ensuring higher generalization and health equity (Anibal et al., 2 Apr 2024, Piao et al., 28 Aug 2025).

In conclusion, automatic health assessment from voice has rapidly progressed, yielding replicable, interpretable, and increasingly robust diagnostic systems that are capable of supporting diverse clinical scenarios, enhancing health equity, and transforming large-scale screening and remote monitoring paradigms.