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Towards clinical adoption of voice and speech as measures of health: the need for harmonization

Nicholas Cummins, Vikram Ramanarayanan, Daniel Low, Fabio Catania, Si-Ioi Ng, Caterina Botelho, Judith Dineley, Abir Elbeji, Julie M. Liss, Paula Andrea Perez-Toro, Visar Berisha, Thomas Quatieri

arXiv:2609.28894v1cs.ETcs.SD

TL;DR

Heterogeneous collection, processing, and analysis practices, alongside variability across clinical contexts, complicate reproducible and comparable speech-health research. The paper proposes harmonized, interpretable measures and supporting resources; it also supplies physiological and perceptual correlates to help translate measure patterns, while noting that rigorous links to clinical constructs remain incomplete.

  • Problem

    Methodological variability across the speech-analysis pipeline and diverse clinical contexts complicates reproducibility, comparison, and harmonization.

    Source: Introduction · p. 2, Spotlight on feature extraction · p. 4

  • Method

    The paper provides a harmonized set of clinically interpretable acoustic measures, American English normative values, precise definitions, and open-source extraction code.

    Source: Introduction · p. 3, Spotlight on feature extraction · p. 6

  • Results

    The paper provides perceptual and physiological correlates that can help translate acoustic measure patterns into interpretable physiological or perceptual patterns.

    Source: §3 · p. 18

  • Takeaways & Limitations

    Measure validity depends on alignment with elicitation tasks and clinical constructs, and standardized tasks and reference norms support meaningful interpretation.

    Source: Introduction · p. 3, Spotlight on feature extraction · p. 6

    Rigorous evidence linking the outlined measures to underlying clinical constructs has not yet been fully established.

    Source: §3 · p. 18

Abstract

from arXiv · show

Speech and voice are multidimensional signals that capture both communicative intent and underlying physiological processes, providing a unique, non-invasive window into health. Analyzing these signals has the potential to yield digital biomarkers that (i) provide scalable, objective measurement tools for research and clinical care and (ii) reflect the presence or progression of diverse conditions, including neurological, psychiatric, respiratory, and cardiovascular disorders. Realizing this promise, however, requires the field to overcome pervasive reproducibility and generalizability issues due to heterogeneous data collection, processing, and analysis practices. A major source of this heterogeneity is how underlying acoustic measures themselves are defined and computed. In this paper, we outline key considerations across the speech biomarker discovery lifecycle, from data collection through machine learning modeling to clinical interpretation, needed to achieve reliable, reproducible, and clinically translatable results. Chief among these is the need for harmonization efforts to start from common, precisely specified measure definitions. As a first step, we therefore provide definitions, physiological correlates, and computational implementations for a minimal, clinically interpretable set of core speech measures spanning respiration, phonation, articulation, and fluency. We close by discussing ongoing standardization efforts and the open challenges that remain in advancing the adoption of speech- and voice-based digital biomarkers.

Introduction

Speech and voice carry communicative and physiological information with potential as scalable health measures, but heterogeneous research practices hinder reproducibility and comparison. The paper proposes harmonized, clinically interpretable measures and resources to support consistent use.

Introduction

  • Speech and voice combine communicative content with physiological, cognitive, and motor information that clinicians already use, while automated and remote assessment offers scalable monitoring potential.Ambient scribing and smartphone or web-based collection support routine or frequent assessment.Source: Introduction · p. 2
  • Diverse and underreported data collection, processing, and analysis practices impede reproducibility and cross-study comparison, while variability across clinical contexts makes universal standardization difficult.The paper frames harmonization as balancing standardization with flexibility across individuals, languages, contexts, methods, and conditions.Source: Introduction · p. 2
  • Harmonization of variables, extraction procedures, study design, and open-science practices can strengthen reproducibility, comparison across studies, and the evidence base for clinical integration.Source: Introduction · p. 2
  • The paper brings together domain experts to outline a structured harmonization process and proposes clinically interpretable measures with definitions, American English norms, and open-source extraction code.The proposed framework emphasizes measure definitions and standardization, with interpretation contingent on alignment between measures, elicitation tasks, and clinical constructs.Source: Introduction · p. 3
  • The paper distinguishes voice, the larynx-generated acoustic signal, from speech, spoken language shaped by articulation and prosody, and uses validity evidence to frame measure interpretation.It adopts “speech measure” as a term that avoids implying an engineered model input or a clinically validated biomarker.Source: Introduction · p. 3

1 Project Lifecycle in Voice and Speech Research for Health

The speech-health research lifecycle runs from study design through data collection, preprocessing, measure extraction, modeling, and clinical deployment. Across that lifecycle, harmonization depends on clinically anchored measures, appropriate elicitation, transparent extraction, and attention to sources of variability.

1 Project Lifecycle in Voice and Speech Research for Health

  • The lifecycle moves from defining constructs, tasks, inclusion criteria, and covariates through recording and preprocessing to measure extraction, modeling, and real-world health-state assessment.Recording choices include microphone and digitization settings; preprocessing should prepare recordings without stripping clinically relevant variability.Source: §1 · p. 4

Spotlight on feature extraction

  • Variability in design, elicitation, recording, and feature extraction can interact, hindering replication and comparison; harmonization therefore centers on clinically anchored, task-appropriate, comparable measures.Source: Spotlight on feature extraction · p. 4
  • Elicitation tasks determine which constructs can be measured: sustained phonation probes phonatory stability, rapid syllable repetition articulatory control, and reading fluency, while mismatched tasks weaken measurement.Phonation measures can be less reliable in free speech because articulation influences them.Source: Spotlight on feature extraction · p. 4
  • Recording devices and conditions affect extracted measures, especially intensity; excessive denoising, dereverberation, segmentation, or diarization can remove relevant signal or introduce bias.Source: Spotlight on feature extraction · p. 6
  • Extraction tool, parameters, frame sizes, and error handling must be documented because nominally identical measures can differ across tools; age- and sex-related variation also requires normative reference distributions.Source: Spotlight on feature extraction · p. 6
  • Audio embeddings lack direct physiological or clinical interpretability and may encode channel, language, or sociodemographic confounds, so the paper places them after interpretable baselines with bias checks and ablations.Source: Spotlight on feature extraction · p. 6
  • The paper’s harmonized measure set and American English norms are intended for construct-specific studies using standardized tasks, with age and sex collected as key covariates.Examples include sustained phonation for phonatory control and read speech for fluency, prosody, and articulation.Source: Spotlight on feature extraction · p. 6

2 Measures and Normative Values

This section presents a reproducible, open-source workflow for extracting core speech and voice measures and normative values from U.S. CLAC recordings, covering their definitions, scope, selection, and interpretation. It describes task-specific patterns across respiration, phonation, articulation, spectral characteristics, and fluency.

2 Measures and Normative Values

  • The toolkit provides core measure descriptions, normative ranges, and open-source code; normative values are limited to directly observable intensity and intensity range.Other measures require additional signals or modeling, so the normative set prioritizes transparent, robust measures.Source: §2 · p. 7, §2.1 · p. 8

2.1 Description of core measures

  • The six measure categories are respiration, phonation, articulation, spectral, fluency, and language, but this paper focuses on acoustic-signal measures rather than transcript-derived language measures.Language measures cover semantic and syntactic structure and are briefly introduced in Appendix S8.Source: §2.1 · p. 7
  • Respiration measures capture speech intensity and respiratory rate, while selected phonation features include F0, Pitch Sigma, Spectral Slope and Tilt, and CPP for remote robustness and task agnosticism.Intensity is a composite measure that can reflect vocal source and filter effects as well as microphone distance and room acoustics; glottal-flow-dependent or channel- and task-sensitive measures are excluded.Source: §2.1 · pp. 7, 8, 10
  • Core articulation measures are formant frequencies and bandwidths, which characterize vocal-tract resonances directly, while speaker-normalized patterns help address anatomical differences.Spectral representations range from time-frequency analyses to compact coefficients, but can also encode noise and dataset artifacts; measures therefore need clearly defined constructs and elicitation tasks.Source: §2.1 · pp. 10–12
  • Fluency measures describe speech speed, breakdown, and repair, can often be extracted from counts, timestamps, and signal-energy thresholds, and require protocol-specific validation to establish biomarker relevance.The categories include delivery rate and density, pauses, and hesitations or repetitions.Source: §2.1 · p. 12

2.2 Open-source code

  • The open-source senselab1 Python example demonstrates a straightforward pipeline for extracting selected clinically relevant measures and choosing them according to speech-task characteristics and conditions.Source: §2.2 · p. 13
  • The open-source senselab example loads audio, downmixes it to mono, resamples it to 16 kHz, and extracts health-related speech measurements.The passage presents the code as an example workflow for comparability across datasets.Source: §2.2 · p. 13

2.3 Normative values for the core measures

  • Normative values were estimated from 1,815 U.S. CLAC participants using maximum phonation and rainbow-passage reading tasks, with measures selected to suit each task.The sample spanned all 50 states and 956 cities; timing and fluency measures were not extracted for sustained phonation, and HNR, jitter, and shimmer were omitted for passage reading.Source: §2.3 · p. 14
  • Intensity SD was approximately 20.0 dB in maximum phonation and 26 dB in reading, while intensity measures are sensitive to microphone placement and environmental noise.The passage also reports higher intensity range ratios in phonation overall, but cautions that intensity should be interpreted carefully.Source: §2.3 · pp. 14–15, Table 6 · p. 15
  • Fundamental frequency averaged 156.6 Hz in maximum phonation and 156.0 Hz in reading, while cepstral peak prominence was higher in phonation than reading.CPP was 11.5 dB overall in phonation and 9.1 dB in reading; HNR averaged 14.0 dB in phonation and was not reported for reading because of phonetic variability.Source: §2.3 · pp. 15–16, Table 7 · p. 16
  • F1 averaged 714.2 Hz in phonation versus 503.6 Hz in reading, while F2 averaged 1289.2 Hz versus 1557.9 Hz.Males had lower formant values than females across tasks, and formant standard deviations were lower in phonation than reading for both genders.Source: §2.3 · pp. 16–17
  • Spectral gravity averaged 741.0 Hz in phonation versus 543.7 Hz in reading, while spectral standard deviation was 416.4 Hz versus 397.9 Hz.Spectral kurtosis and skewness were lower in phonation; females had slightly higher spectral standard deviations than males in both tasks, attributed to higher F0 and wider harmonic spacing.Source: §2.3 · p. 17
  • Maximum phonation averaged 16.3 seconds, while reading averaged 14.7 seconds and females had slightly faster articulation and speaking rates than males.In reading, females’ articulation rate was 4.4 versus 4.3 syllables/second and speaking rate was 3.9 versus 3.7 syllables/second; mean pause duration was 0.6 seconds overall.Source: §2.3 · p. 17

3 Discussion and Future Outlook

The paper advances harmonization through speech measures, extraction code, and normative values, while emphasizing that links between measures and clinical constructs still require rigorous validation. It also identifies interpretability and consistent standards as needs for translating this work into clinical research and practice.

3 Discussion and Future Outlook

  • The work supplies speech measures, extraction code, and normative values for read speech and sustained vowels as a step toward reproducible biomarker research.The broader harmonization effort also applies to mechanistic and causal speech research and early translational work linking speech constructs to health.Source: §3 · p. 17
  • The code provides a starting point for extracting a bespoke measure space tailored to the elicitation task and clinical outcome.Source: §3 · p. 17
  • Links between common speech measures and underlying clinical constructs are not yet fully established, so rigorous quantitative validation remains a research need.The code is intended to evolve through evidence-based methodological improvements.Source: §3 · p. 18
  • Perceptual and physiological correlates, such as low f0 variance with monotonous speech, can help make acoustic measure patterns more interpretable beyond speech science.Source: §3 · p. 18
  • Agreed standards for design and comparison can support large-scale replication and robust validation toward clinical integration of speech and voice biomarkers.Source: §3 · p. 18

Disclaimer

The section provides guidance on the speech-biomarker lifecycle and considerations for evaluating speech and voice measures for clinical use, including real-world benefit, bias, opacity, and generalizability. It also notes that the authors’ opinions and conclusions do not necessarily reflect the Department of the Air Force’s views.

S1 Appendix: Study Design

  • Study protocols should specify the target condition and task, measurable speech manifestations, participant and collection criteria, and success metrics; preregistration clarifies hypotheses and outcomes.The appendix recommends publicly available protocols to support reproducibility and harmonization.Source: Disclaimer · pp. 18–19

Existing Benchmarks

  • Existing challenges provide shared test beds, but Pitt Corpus confounds and C19C recording biases warrant caution and, where possible, new data collection.The appendix identifies ComParE, AVEC, and Alzheimer’s-focused challenges as examples of existing benchmarks.Source: Disclaimer · p. 19

S2 Appendix: Speech Elicitation Considerations

  • Elicitation tasks should target relevant symptoms: DDK and reading tasks can reveal speech-motor deficits, while naming and picture description can assess neurocognitive impairment.Task planning should also consider practice effects, ordering, acceptability, instructions, and preparation workload.Source: Disclaimer · pp. 19–20

S3 Appendix: Data Collection

  • Recording devices, environments, digitization, speakers, and behaviors can introduce measurement error; harmonized protocols and speaker metadata help reduce bias and improve generalizability.Relevant metadata can include age, sex, languages, education, medications, and health or comorbidities.Source: Disclaimer · p. 21

S4 Appendix: Pre-processing Considerations

  • Preprocessing can alter clinically relevant information: denoising may distort spectral measures, while ASR can struggle with atypical speech, remove disfluencies, or hallucinate.Because each preprocessing step can introduce and propagate uncertainty, the appendix recommends minimizing such steps and reporting results with and without them.Source: Disclaimer · pp. 21–22

S5 Appendix: Feature Extraction

  • Toolkit and measure choices can change extracted results; clinical representations should match the population, context, task, and relevant constructs to support interpretability and generalization.The appendix calls for evaluation across diverse tasks, populations, and conditions rather than reliance on narrowly defined measure sets.Source: Disclaimer · p. 22

S6 Appendix: Data Analysis

  • Statistical analysis tests associations, whereas machine learning targets prediction on unseen data; nested cross-validation helps prevent leakage during model selection and tuning.The appendix also cautions that weak or meaningless baselines can create over-optimistic expectations.Source: Disclaimer · p. 23

S7 Appendix: Health State Assessment

  • Clinical assessment requires temporal consistency, interpretability, and real-world evaluation; XAI describes model behavior but does not establish a clinically meaningful or causal relationship.Test–retest reliability requires measurements to remain consistent while the underlying health state is stable, a challenge noted in the appendix.Source: Disclaimer · pp. 23–24

Real-World Implementation

  • Updated held-out evaluations are crucial because measure–outcome relationships can shift across demographics, disease severity, comorbidities, extraction methods, and recording devices; later-stage assessment should consider net benefit.Biomarker-guided decisions can affect treatment initiation and costly follow-ups, so evaluation should establish that they improve rather than compromise patient care.Source: Disclaimer · p. 24

S8 Appendix: Language Processing

  • Transcript-based measures may serve as cognitive or language biomarkers, while NLP methods trade interpretability against context handling and semantic analysis; embedding similarities have correlated with clinical ratings.LLMs can produce categorical scores and rationales, but their underlying architecture may not be fully interpretable.Source: Disclaimer · pp. 24–25

S9 Appendix: Learnable Data Representations

  • Audio embeddings may encode non-health information, and proprietary black-box models raise inclusivity and generalizability concerns, so bias analysis is needed to avoid overestimated performance.The field is dominated by grey literature, and transparent evaluations on clinical data remain rare.Source: Disclaimer · pp. 25–26
  • Interpretable dimensions learned from high-dimensional audio may offer clinical insights, but their extraction remains opaque, so clinical validity requires evaluation and results warrant caution.Neural approaches can map high-dimensional measures into an interpretable layer within an overall deep neural network.Source: Disclaimer · p. 26

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