WATCH-SS: A Trustworthy Approach to Cognitive Health Monitoring Through Speech Analysis

In today’s healthcare landscape, early detection of cognitive impairment remains one of the most critical challenges we face. Traditional assessment methods often require in-person evaluations by specialists, creating barriers to widespread screening and timely intervention. What if there was a more accessible way to monitor cognitive health? Enter WATCH-SS—a promising new framework that could revolutionize how we approach cognitive screening.

Understanding WATCH-SS: More Than Just Another AI Tool

WATCH-SS stands for “Warning Assessment and Alerting Tool for Cognitive Health from Spontaneous Speech.” This isn’t just another artificial intelligence application; it represents a thoughtful approach to cognitive health monitoring that prioritizes trustworthiness and interpretability—two qualities that are essential when dealing with sensitive health information.

Unlike many “black box” AI systems that provide results without explanation, WATCH-SS is designed from the ground up to be transparent. When it identifies potential cognitive concerns, it doesn’t just give a yes-or-no answer—it shows its work, allowing healthcare professionals to understand exactly why certain conclusions were reached. This level of transparency is crucial in medical applications where decisions can significantly impact people’s lives.

Currently, the research behind WATCH-SS is undergoing peer review, with a preprint version available on medRxiv. This means the scientific community is actively examining and validating the approach, which is an important step before any tool can be widely adopted in clinical practice.

Why Speech Matters in Cognitive Assessment

You might be wondering: why focus on speech? The connection between how we speak and our cognitive health is deeper than many realize. When we communicate, our brains engage multiple cognitive processes simultaneously:

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Retrieving appropriate words from memory
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Constructing grammatically correct sentences
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Maintaining coherent thought flow
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Monitoring our speech for errors
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Adjusting communication based on listener feedback

When cognitive function begins to decline, these processes can become disrupted, often showing up in subtle changes to speech patterns long before more obvious symptoms appear. These changes might include:

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Increased pauses between words
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Simplified sentence structures
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Reduced vocabulary diversity
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Difficulty finding the right words
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Problems maintaining conversational coherence

What makes WATCH-SS particularly valuable is that it analyzes spontaneous speech—how people naturally talk in everyday conversations—rather than requiring specific tasks or memorization exercises. This approach creates a more comfortable, less stressful experience for patients while capturing authentic communication patterns.

The Building Blocks of WATCH-SS

Let’s explore how WATCH-SS is structured. The framework follows a modular design, with each component serving a specific purpose. This organization isn’t just about technical neatness—it makes the system more adaptable, maintainable, and understandable.

Data Processing: The Foundation

At the heart of WATCH-SS lies the data/ directory, which contains code for loading and preprocessing two important datasets: ADReSS and OBSERVER. These datasets consist of speech samples from individuals with and without cognitive impairment, providing the raw material the system uses to learn and make assessments.

The data processing component handles several critical tasks:

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Standardizing audio formats across different recording devices
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Segmenting speech into analyzable units
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Anonymizing personal information to protect privacy
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Organizing samples according to cognitive status
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Preparing the data for analysis by the detection modules

Without this careful data preparation, the system couldn’t reliably distinguish between normal speech variations and meaningful indicators of cognitive changes.

Detection Modules: The Analytical Engine

The detectors/ directory houses the core functionality of WATCH-SS—the algorithms that actually identify potential signs of cognitive impairment in speech samples. These detectors work together to examine different aspects of speech, creating a comprehensive picture of cognitive health.

Each detector focuses on specific features that research has linked to cognitive function:

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Fluency detectors analyze speech rhythm, identifying unusual pauses or repetitions
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Lexical detectors examine vocabulary richness and word choice patterns
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Syntactic detectors assess sentence structure complexity and grammatical accuracy
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Semantic detectors evaluate how well ideas connect and flow within speech

By using multiple specialized detectors rather than a single monolithic algorithm, WATCH-SS can provide nuanced insights while maintaining interpretability. If a concern is flagged, clinicians can see which specific aspects of speech contributed to the finding.

Development Environment: Where Innovation Happens

The notebooks/ directory contains Jupyter notebooks, which serve as the workshop where researchers develop and refine the detection algorithms. These interactive documents combine code, visualizations, and explanatory text, allowing developers to:

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Test new detection approaches
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Visualize how different speech features correlate with cognitive status
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Compare the performance of various algorithm configurations
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Document their findings and methodology

This transparent development process ensures that improvements to WATCH-SS are based on clear evidence and thorough testing, rather than opaque “black box” tuning.

Supporting Infrastructure: The Glue That Holds It Together

Several other components ensure WATCH-SS functions smoothly:

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utils.py: This utility file contains shared functions that multiple parts of the system use, preventing code duplication and ensuring consistency
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compute_init.sh: This script simplifies setup on Microsoft Azure Databricks, making it easier to run WATCH-SS on cloud infrastructure
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requirements.txt: This critical file lists all the Python libraries WATCH-SS depends on, ensuring consistent results across different computing environments

These supporting elements might seem technical and behind-the-scenes, but they’re essential for making WATCH-SS reliable, reproducible, and accessible to researchers worldwide.

Comprehensive Documentation: Knowledge Sharing

WATCH-SS comes with thorough documentation in two formats:

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supplementary_material.pdf: A detailed PDF document expanding on the methodology and findings
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supplementary_material.md: The same information in Markdown format, which is easier to read and edit

This dual-format approach ensures that researchers can access the information in whatever format suits their needs best, whether they’re reading on screen or printing for reference.

How WATCH-SS Works in Practice

Understanding the technical components is valuable, but how does WATCH-SS actually function when used with real speech samples? While the exact implementation details are in the preprint paper, we can outline the general workflow based on the system’s structure.

The Analysis Process

When a speech sample is provided to WATCH-SS, here’s what happens:

Data Preparation: The system processes the audio file, converting it to a standardized format and extracting relevant features
Multi-Detector Analysis: Each detector in the detectors/ directory examines the speech for specific patterns associated with cognitive health
Evidence Aggregation: The system combines findings from all detectors, weighing each according to its reliability and relevance
Interpretable Reporting: Rather than just providing a binary “impaired/not impaired” result, WATCH-SS generates a detailed report showing which speech features contributed to the assessment

This process creates a more nuanced understanding than simple pass/fail testing. Clinicians don’t just get a result—they get evidence they can evaluate themselves.

Implementation Requirements

For researchers or developers interested in working with WATCH-SS, here’s what you’ll need:

Technical Setup
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  Python 3.7 or newer
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  Dependencies listed in requirements.txt (installed via pip install -r requirements.txt)
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  Optional: Microsoft Azure Databricks for cloud deployment (using compute_init.sh)
Data Requirements
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  Speech samples in compatible audio formats
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  ADReSS or OBSERVER datasets for training/validation (access may require approval)
Processing Environment
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  Adequate computing resources for audio processing
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  Storage for speech samples and analysis results

The modular design means you don’t need to implement the entire system at once. Researchers can start with specific components that match their current needs and expand as required.

Why WATCH-SS Stands Out in Cognitive Assessment

Several features make WATCH-SS particularly noteworthy in the field of cognitive health assessment:

Trust Through Transparency

Many AI health tools operate as “black boxes,” making decisions without revealing their reasoning. In healthcare, this lack of transparency is problematic—clinicians need to understand why a system reached a particular conclusion before acting on it.

WATCH-SS addresses this by making its decision process visible. When it identifies potential cognitive concerns, it shows exactly which speech patterns contributed to that finding. This transparency builds trust with healthcare professionals and allows for more informed clinical judgment.

Modular Flexibility

The system’s modular architecture offers significant advantages:

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Customization: Clinicians can select which detectors to use based on their specific needs
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Upgradability: Individual components can be improved without overhauling the entire system
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Specialization: Researchers can develop detectors focused on particular aspects of cognitive function
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Integration: Components can potentially be incorporated into existing clinical workflows

This flexibility means WATCH-SS can adapt to different clinical settings and research questions without requiring a complete rebuild.

Real-World Applicability

By focusing on spontaneous speech—the way people naturally talk in everyday conversations—WATCH-SS avoids many pitfalls of traditional cognitive tests:

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No need for specialized testing environments
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Less intimidating for patients than formal exams
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Captures authentic communication patterns
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Can be administered remotely via phone or video calls
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Requires minimal additional time during routine appointments

These practical advantages could significantly increase screening rates and enable earlier interventions.

Potential Applications of WATCH-SS

While still in the research phase, WATCH-SS shows promise for several important applications:

Primary Care Screening

Imagine a routine doctor’s visit where, after discussing symptoms, the physician asks a few follow-up questions while the system analyzes the conversation. This unobtrusive screening could identify patients who might benefit from more comprehensive evaluation, all without adding significant time to the appointment.

Longitudinal Monitoring

For patients already diagnosed with mild cognitive impairment, regular speech analysis could track progression more sensitively than periodic formal testing. Small changes in speech patterns might indicate whether an intervention is working or if the condition is advancing.

Clinical Trial Endpoints

In drug trials for cognitive disorders, WATCH-SS could provide objective, quantifiable measures of treatment effectiveness. Unlike subjective assessments, speech analysis offers consistent metrics that aren’t influenced by examiner bias.

Telehealth Integration

As telemedicine grows, tools like WATCH-SS could enhance remote cognitive assessments. During video consultations, the system could analyze speech patterns to provide additional insights that might be missed in virtual interactions.

Addressing Common Questions About WATCH-SS

Let’s address some questions you might have about this emerging technology.

How does WATCH-SS differ from other speech analysis tools?

Many speech analysis tools focus on a single aspect of speech or provide only a general assessment. WATCH-SS stands out through its:

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Modular design with specialized detectors for different cognitive indicators
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Commitment to interpretability—showing exactly why certain conclusions were reached
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Focus on spontaneous, natural speech rather than structured tasks
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Integration of multiple evidence streams for more reliable assessment

Is WATCH-SS ready for clinical use?

Not yet. The manuscript describing WATCH-SS is currently undergoing peer review, which means it’s still in the research validation phase. While the preprint is available for examination, the tool hasn’t yet received regulatory approval for clinical diagnosis. It should be considered a research tool at this stage.

What technical skills are needed to work with WATCH-SS?

To implement the full system, you’ll need:

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Proficiency in Python programming
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Understanding of machine learning concepts
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Experience with audio processing
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Familiarity with scientific computing environments

However, future implementations might offer more user-friendly interfaces that require less technical expertise for clinical use.

How can researchers access WATCH-SS?

Based on the information provided, the code and materials appear to be available alongside the preprint paper on medRxiv. Researchers interested in using WATCH-SS should refer to the paper’s availability statement for specific access instructions.

What datasets does WATCH-SS use?

WATCH-SS works with two specialized datasets:

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ADReSS: A dataset specifically designed for Alzheimer’s Disease Recognition through Spontaneous Speech
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OBSERVER: Another dataset focused on cognitive assessment through speech analysis

These datasets contain speech samples from individuals with and without cognitive impairment, allowing the system to learn distinguishing patterns.

How should WATCH-SS be cited in research?

If you use WATCH-SS in your research, you should cite it using the provided reference:

@article {pugh2025watchss,
    author = {Pugh, Sydney and Hill, Matthew and Hwang, Sy and Wu, Rachel and Jang, Kuk and Iannone, Stacy L and O'Connor, Karen and O'Brien, Kyra and Eaton, Eric and Johnson, Kevin B},
    title = {WATCH-SS: A Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech},
    elocation-id = {2025.08.06.25333047},
    year = {2025},
    doi = {10.1101/2025.08.06.25333047},
    publisher = {Cold Spring Harbor Laboratory Press},
    URL = {https://www.medrxiv.org/content/early/2025/08/08/2025.08.06.25333047},
    eprint = {https://www.medrxiv.org/content/early/2025/08/08/2025.08.06.25333047.full.pdf},
    journal = {medRxiv}
}

Can WATCH-SS replace traditional cognitive assessments?

No—and it’s not designed to. Instead, WATCH-SS should be viewed as a complementary tool that can:

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Identify individuals who might benefit from more comprehensive evaluation
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Provide objective data to support clinical judgment
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Enable more frequent monitoring between formal assessments
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Reduce barriers to initial screening

Traditional cognitive assessments remain essential for diagnosis and detailed evaluation.

What are the limitations of speech-based cognitive assessment?

While promising, this approach has some limitations to consider:

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Speech patterns can be affected by factors unrelated to cognition (fatigue, emotional state, hearing difficulties)
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Different languages and dialects may require specialized models
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Cultural differences in communication styles need careful consideration
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Certain neurological conditions affect speech production independently of cognition

These factors highlight why interpretability is so important—clinicians need to understand the context of any findings.

The Future of Cognitive Assessment

WATCH-SS represents an important step toward more accessible, objective cognitive health monitoring. As research continues, we might see:

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Integration with everyday communication devices (smartphones, smart speakers)
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Development of language-specific models for global applicability
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Combination with other digital biomarkers for more comprehensive assessment
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Refinement of detectors to identify specific types of cognitive impairment

The emphasis on trustworthiness and interpretability sets a valuable precedent for medical AI development. Rather than chasing maximum accuracy at the expense of transparency, WATCH-SS demonstrates that responsible AI design considers both performance and understandability.

Implementing WATCH-SS: A Step-by-Step Guide

For researchers interested in working with WATCH-SS, here’s a practical implementation guide based on the provided information:

Step 1: Set Up Your Environment

Ensure you have Python 3.7 or newer installed

Create a virtual environment to isolate dependencies

python -m venv watchss-env
source watchss-env/bin/activate  # Linux/Mac
watchss-env\Scripts\activate    # Windows

Install required dependencies:
```
pip install -r requirements.txt
```

Step 2: Prepare Your Data

Obtain the ADReSS or OBSERVER datasets (following their respective data use agreements)
Organize your speech samples according to the expected directory structure
Ensure audio files are in compatible formats (specific formats would be detailed in the documentation)

Step 3: Run the Analysis

You have two main options:

Option A: Using Jupyter Notebooks

Launch Jupyter: jupyter notebook
Navigate to the notebooks/ directory
Open and run the relevant notebooks for your needs

Option B: Command Line Processing

Use the scripts in data/ to preprocess your speech samples
Apply the detectors from detectors/ to analyze the processed data
Generate reports using the utility functions in utils.py

Step 4: Interpret Results

When reviewing WATCH-SS output, pay attention to:

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Which specific detectors flagged potential concerns
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The strength of evidence for each finding
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Visualizations showing how speech patterns compare to reference data
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Any confidence metrics provided by the system

Remember that these results should inform, not replace, clinical judgment.

Ethical Considerations in AI-Powered Cognitive Assessment

As with any medical technology, WATCH-SS raises important ethical questions:

Privacy Protection

Speech contains highly personal information. Proper implementation must ensure:

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Strict data anonymization
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Secure storage of audio recordings
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Clear patient consent regarding data usage
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Compliance with healthcare privacy regulations (HIPAA, GDPR, etc.)

Avoiding Bias

The system must be evaluated across diverse populations to ensure:

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Equal performance across different demographic groups
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Recognition of normal speech variations within different cultural contexts
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Avoidance of false positives/negatives based on accent or dialect

Clinical Responsibility

Even with advanced tools, the ultimate responsibility remains with healthcare providers:

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Results should inform, not dictate, clinical decisions
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Patients should receive clear explanations of how assessments were made
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Human oversight must remain central to the diagnostic process

Looking Ahead: The Path to Clinical Integration

For WATCH-SS to move from research tool to clinical asset, several steps remain:

Peer Review Completion: The current manuscript under review will undergo scientific scrutiny
Validation Studies: Independent verification across diverse populations
Regulatory Approval: Meeting standards for medical devices in relevant jurisdictions
Clinical Workflow Integration: Designing practical implementations for real-world settings
Provider Education: Training healthcare professionals on appropriate use and interpretation

This progression ensures that any clinical implementation is based on solid evidence and careful consideration of real-world needs.

Conclusion: A Thoughtful Approach to Cognitive Health

WATCH-SS represents more than just a technical achievement—it embodies a philosophy of responsible AI development for healthcare. By prioritizing trustworthiness and interpretability alongside technical performance, it sets a standard for medical AI that puts patient care first.

While still in the research phase, the framework offers a glimpse of a future where cognitive health monitoring is more accessible, objective, and integrated into routine care. As validation continues and the technology evolves, tools like WATCH-SS could play an important role in early detection and management of cognitive conditions.

For healthcare professionals, researchers, and developers, WATCH-SS serves as both a practical tool and a model for how AI can thoughtfully augment human expertise in medicine—providing valuable insights while respecting the complexity of clinical decision-making.

WATCH-SS: How Your Speech Patterns Could Revolutionize Early Cognitive Impairment Detection