Acoustic and Temporal Computational Approaches for Early Identification of Psychological Disorders: A Human-Centric Survey

Abstract

As the world is growing faster, psychological disor- ders are aslo increasing and affecting people of all ages, cultures and financial standards. Since more people face these struggles, earlysigndetectionmatterstoensureanearlyinterventionbefore the problem worsens. Traditional diagnosis usually depends on personal judgment, talking with doctors or patients describing their feelings but that does not work well in cases when one cannot speak up, if a person is too young or is losing memorywithageorfindsithardtocommunicate.Somehidehowtheyfeel out of fear of being judged or even due to the misconception and pressure by others surrounding them. These days, advances in technologies like AI, audio processing, temporal pattern analysis andbehaviormodelingmakeearlydiagnosismoreandmoreeasy by passively observing daily activities of the people. This paper discusses the capability of computers in identifying two types of natural data; voice sounds produced while speaking and words written or shared online across social networks. Voice signs such as in+ -9stability of pitch, irregular pacing, tension in the voice and pausingareamongothermeasurablefeaturesofspeechthat can be indicative of physical or mental states if one doesn’t say anything outright. Meanwhile changes in the pattern of writing online, the mood conveyed in these posts, word choices, context clues and manner of expression over days reveal mental health tendencies. A key focus here is timing. Mental health decline usuallydoesn’thitquickly,thesignstendtoshiftslowly,showing up over time instead of in a single moments. Combining voice tech with written behavior clues shows promise in noticing issues sooner without invading their private space or cutting access. This review brings together basic studies of new deep learning ideas, ways to combine different data types and also making models easier to understand while pointing out open issues like smalllabeleddatasets,moralquestions,problemsapplyingresults broadly, plus hurdles in real world medical use. Its objective isto give a clear picture of today’s methods, show how sound and analyzing it over time could help spot mental health needs and help build smarter tools that adapt well across different people, include diverse groups, act ethically.

Introduction

Mental health disorders such as depression, anxiety, ADHD, bipolar disorder, and memory issues are increasing worldwide. These conditions reduce productivity, damage long-term health, strain families, and increase disability. Early detection is essential, but traditional diagnosis depends heavily on self-reporting, verbal expression, and access to specialists. Many people hide symptoms, cannot express emotions, or fail to recognize early warning signs.

Because everyday speech and writing carry subtle emotional cues, modern AI and deep learning now offer new ways to detect psychological changes. Advanced systems can analyze voice patterns—like pauses, pitch, pacing, and stress—and even track social media activity and language use. Studies such as the eRisk challenge show that linguistic analysis can predict risk of depression, eating disorders, and self-harm before clinical diagnosis.

A key advancement is temporal modeling, which focuses on how behavior changes over time rather than in a single moment. Tracking gradual shifts in tone, rhythm, or emotional cues allows earlier prediction of mental health risks.

Proposed Methodology (Acoustic AI)

The system uses natural, unscripted speech captured from real-life conversations. It focuses on how a person speaks, not what they say. The pipeline includes:

1. Audio Collection & Processing
   Natural speech is recorded in daily environments. Privacy is protected by removing personal identifiers and background clues. Audio is cleaned, segmented, and standardized.

2. Acoustic Feature Extraction
   The system extracts indicators that reflect psychological states:

   • Pitch variation

   • Speech pacing (fast/slow speech)

   • Pause patterns

   • Jitter (short-term sound instability)

   • Shimmer (volume fluctuations)

   • MFCCs and other low-level descriptors

   These features help identify stress, sadness, anxiety, fatigue, and cognitive decline.

3. Temporal Modeling
   Speech is analyzed across multiple time segments to detect gradual mood changes.
   Using RNNs, transformers, and attention mechanisms, the model aligns and compares voice traits across days or weeks. This supports personalized, long-term monitoring rather than one-time assessments.

4. Classification & Evaluation
   Deep neural networks (CNNs, RNNs, transformer encoders) classify speech patterns into risk categories. The system measures severity trends, confidence, and robustness under real-world conditions. It is designed to assist clinicians, not replace them.

Distinct features:

• Works even with minimal or fragmented speech

• Highly privacy-centric

• Tailored deep-learning models

• Focuses on long-term behavioral change

• Ethical and non-invasive

Novelty and Contributions

1. Integration of speech and text analysis
   The review connects acoustic signals with linguistic cues, showing how both reveal psychological changes over time.

2. Temporal modeling as the core idea
   Mental health shifts gradually. Tracking speech and language over time provides deeper insights than single snapshots.

3. Comprehensive survey (2023–2025)
   The review covers major advancements in speech analysis, language models, transformer architectures, GNNs, and multimodal fusion.

4. Challenges & Future Work

   • Data scarcity

   • Generalization across cultures and accents

   • Privacy concerns

   • High computational requirements

   • Need for explainable AI

   The text suggests future directions such as lightweight models, diverse datasets, explainability tools, and ethical deployment frameworks.

5. Key Contributions Summary

   • Unifies speech- and text-based mental health detection

   • Highlights importance of long-term tracking

   • Combines modern AI research trends

   • Provides practical and ethical guidelines for future tools

Literature Review

The review organizes recent research into five major areas:

1. Foundational Speech Emotion Recognition (SER)

Early work focused on recognizing emotions through acoustic features. Models struggled with diverse languages and environments. Self-supervised models like Wav2Vec2 improved cross-lingual performance.

2. Advanced Deep Learning & Multimodal Fusion

Modern systems combine speech, text, physiological data, and behavior cues using transformers, GNNs, and hypergraph models. LLM-based methods now detect multiple disorders via speech and context.

3. Low-Resource & Efficient Models

Temporal linguistic analysis (TWEC, DCWE) helps detect early mental health changes. Context-aware models capture conversational cues but require significant memory.

4. Temporal & Context-Aware Modeling

Models analyze long sequences of speech to detect slow behavioral shifts. Transformers and RNNs track speech flow, rhythm, and semantic drift over long periods.

5. Model Explainability

Clinically acceptable systems must be transparent. Techniques like attention visualization, post-hoc mapping, and probabilistic modeling help explain predictions, improving trust and safety.

Conclusion

This project gives us a new way of understanding how computers can detect the early signs of mental illness from parameters such as speech and language. It looks at how sensors,behaviouralmodels,multimodallearningthattakesin many types of signals at once and machine learning systems help to improve this field. We use many different kinds of signals and time based methods to predict mental health more accurately, instead of only using emotions. This discussion tells us that mood never just jumps and that it takes a slowpath before it becomes easy to see the warning signs. It shows that speech has special benefits as a way to find information that is not painful to get and often occurs by itself. Speech biomarkerslikepitchvariation,pausetimeandtheenergy of the voice can provide clues about emotional pressures or thinkingoverloadswithoutsayingmuch;wecanlookatonline habits and what people are saying to see how they are feeling. When adding a time element to both of these modalities, This makes it much easier to catch signs of abnormality early on and helping identify psychological risks before they grow into moreseriousproblems.

References

[1] M.Coutoetal.,“TemporalWordEmbeddingsforEarlyDe-tectionofPsychologicalDisordersonSocialMedia,”Journalof Healthcare Informatics Research, 2025. [Online]. Available:https://doi.org/10.1007/s41666-025-00186-9 [2] J. Qin et al., “Mental-Perceiver: Audio-Textual Multi-Modal Learningfor Estimating Mental Disorders,” arXiv preprint arXiv:2408.12088,2024. [Online]. Available: https://arxiv.org/abs/2408.12088 [3] D.Kounadis-Bastianetal.,“Wav2Small:DistillingWav2Vec2to72KParametersforLow-ResourceSpeechEmotionRecogni-tion,” arXiv preprint arXiv:2408.13920, 2024. [Online]. Available:https://arxiv.org/abs/2408.13920 [4] X. Zhang et al., “SpeechT-RAG: Reliable Depression Detection inLLMs with Retrieval-Augmented Generation Using Speech Timing In-formation,”arXivpreprintarXiv:2502.10950,2025.[Online].Available:https://arxiv.org/abs/2502.10950 [5] A. I. S. Ferreira et al., “Enhancing SER with Graph-Based MultimodalFusion and Prosodic Features,” arXiv preprint arXiv:2506.02088, 2025.[Online]. Available: https://arxiv.org/abs/2506.02088 [6] J.Qinetal.,“Context-AwareDeepLearningforMulti-ModalDepressionDetection,” in Proceedings of Conference, 2024. [7] Z. Huang et al., “Efficient Long Speech Sequence Modelling for Time-Domain Depression Level Estimation,” in Proceedings of Conference,2025. [8] Y.Wangetal.,“IdentificationofDepressionStateBasedonMulti-ScaleAcoustic Features,” in Proceedings of Conference, 2023. [9] S. Pal et al., “Interpretable Probabilistic Identification of Depression inSpeech,” in Proceedings of Conference, 2025. [10] M. Atmaja et al., “Explaining Deep Learning Embeddings for SpeechEmotion Recognition by Predicting Interpretable Acoustic Features,” inProceedings of Conference, 2024. [11] S.Latifetal.,“Cross-LingualSpeechEmotionRecognition:Humansvs.Self-Supervised Models,” in Proceedings of Conference, 2024 (revised2025). [12] K. Z. Li et al., “Decoding Emotion: Speech Perception Patterns in Indi-viduals with Self-reported Depression,” in Proceedings of Conference,2024. [13] T. T. Tran et al., “MEDUSA: A Multimodal Deep Fusion Multi-StageTraining Framework for Speech Emotion Recognition in NaturalisticConditions,” in Proceedings of Conference, 2025. [14] Y. Wang et al., “Integration of Text and Graph-based Features forDetecting Mental Health Disorders from Voice,” in Proceedings ofConference, 2024.

Copyright

Copyright © 2025 Vijay Kumar S, Nidhi R. Kashyap, Raveena N, Chandana N. V.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.