The Utility of Speech and Language Analytics for Screening Alzheimer’s Disease

Abstract

Background

Effective screening and cohort enrichment remain major challenges in clinical trials for Alzheimer’s disease (AD), where traditional diagnostic pathways rely on costly, invasive, and time-consuming procedures. Speech and language analysis has emerged as a scalable, low-burden approach for detecting subtle cognitive-linguistic and motor-speech changes that may appear early in the disease course.

Summary

This review synthesizes current evidence on acoustic, prosodic, lexical, semantic, and syntactic speech features associated with AD and mild cognitive impairment (MCI) and evaluates their reported utility across a range of elicitation tasks including picture description, verbal fluency, narrative recall, spontaneous speech, and reading. Across studies, machine-learning models trained on speech and language features have reported consistent performance, although results vary substantially depending on task design, feature sets, and cohort characteristics. Task-dependent variability is evident, with picture description and verbal fluency tasks capturing lexical-semantic and timing markers, while narrative and spontaneous speech tasks capture impairments in coherence, information content, and prosody. Hybrid approaches integrating hand-crafted and machine-extracted features have also been explored to improve interpretability and model performance. Speech and language analytics may support digital prescreening, cohort enrichment, and quality-assurance monitoring within clinical trials; however, their application depends on methodological considerations and validation across diverse settings.

Key Messages

Despite encouraging findings, several methodological challenges persist, including interindividual variability, limited dataset sizes, differences in recording conditions, and limitations in automatic speech recognition performance in cognitively impaired populations. Continued development of standardized protocols, disorder-adapted speech models, and multimodal analytic pipelines is needed to support clinical translation. Collectively, current evidence suggests that speech and language features represent candidate digital markers that may improve screening efficiency and support clinical trial enrichment in AD, although further validation is required to establish their reliability and generalizability.

Introduction

The success of a clinical trial is dependent on effective eligibility screening of participants [1–4]. In neurodegenerative diseases, screening is particularly challenging due to patient heterogeneity, complex diagnostic workflows, and the requirement for biomarker confirmation [5]. Improved patient screening in pharmaceutical clinical trials enhances trial efficiency, reduces costs, and improves data quality, while ensuring patient safety and reducing time to market. In Alzheimer’s disease (AD), the increasing emphasis on early symptomatic and preclinical stages has intensified the need for scalable, low-burden screening tools that can help identify individuals most likely to benefit from further diagnostic evaluation.

According to the 2023 NIA-AA (National Institute on Aging and Alzheimer’s Association) research framework, AD is defined biologically by amyloid and tau pathology, independent of clinical symptoms. “Symptomatic AD,” by contrast, refers to individuals in whom cognitive impairment is attributable to AD pathology [6]. Consistent use of these terms is essential for interpreting digital behavioral markers in relation to disease biology. However, access to biomarker confirmation (e.g., CSF, PET, plasma assays) remains limited, and many individuals with early AD may remain undetected due to subtle symptoms, specialist shortages, and limited geographical access [7]. This gap has motivated the development of objective, remote-capable tools that can enrich pre-screening pathways by identifying individuals who may demand definitive biomarker assessment.

Speech and language hold potential as an objective clinical marker for screening of neurodegenerative disorders like AD [8–11]. Speech production depends on integrated cognitive-linguistic and motor processes, including lexical retrieval, semantic selection, syntactic planning, prosody, articulation, and respiratory-phonatory control as illustrated in Figure 1 [12, 13]. Disturbances in these subsystems may be associated with measurable changes in speech timing, pausing, lexical diversity, syntactic complexity, discourse coherence, and information content. Such alterations in speech have been reported to emerge before other clinical symptoms, with subtle shifts in voice quality, pitch, rhythm, or articulation observed not only in dementia [12] but also in dominantly inherited neurodegenerative conditions such as Huntington’s disease [14] and spinocerebellar ataxia [15]. In this review, we distinguish between speech features, referring to motor-acoustic properties of the speech signal (e.g., pitch, articulation, timing), and language features, reflecting cognitive-linguistic processes (e.g., lexical retrieval, syntax, and semantics). When referring to integrated processes involving both domains, we use the term spoken language production.

Fig. 1.

Illustrative schema for speech production. Conceptualization (thought and ideas formation, selection of appropriate words, organization), language planning (retrieving words from working memory, accessing grammatical rules, planning the structure of utterance), generation of neuromuscular commands, motor programming (planning and execution of precise coordination of muscles to produce speech), respiration (flow of air from the lungs to the trachea and the larynx which causes the vocal cords to vibrate), articulation (movement of the organs of the vocal tract including the tongue, lips, jaw, and vocal cords to produce specific speech sounds), phonation (vocal folds in the larynx vibrate producing sound, adjustment of the tension and length of the vocal folds to control the pitch and volume), prosody (variation in the pitch, intonations, stress pattern, loudness, pausing, and rhythm), feedback loop (auditory and proprioceptive feedback informs subsequent motor planning and execution to ensure smooth and accurate speech) [16–20].

Digital health technologies have the potential to enhance clinical trial conduct by reducing barriers such as geographical proximity, increasing patient diversity, and reducing patient burden [21–23]. Digital speech and language analytics typically involve two complementary analytic streams: acoustic processing of speech signal and NLP applied to its textual transcription. Acoustic markers include pitch, timing, pausing, loudness, and voice quality, while linguistic markers encompass lexical, syntactic, semantic, and discourse-level attributes [24, 25]. Modern ML, large language models, and explainable AI approaches (e.g., SHAP) [26] have improved interpretability and performance of speech-based classifiers. Recent ASR systems (e.g., Whisper, wav2vec2) have shown improved performance and potential robustness to atypical speech, particularly when fine-tuned, addressing earlier limitations noted in the literature [27–29]. Machine-learning algorithms can reduce subjective variability in speech and language assessment; however, careful model development and validation are required to mitigate potential algorithm bias. Intensive development in automation of these analyses has shown promise in environments where outcomes do not need to be fully explainable or transparent (depending on the quality of the data used for building the model). Clinical interviews, for example, provide an opportunity for passively collecting speech data alongside essential clinical information. This para-interview information may help inform patients’ suitability and provide data on performance. Figure 2 describes the pipeline and challenges present at each stage of the process.

Fig. 2.

Processes involved in screening participants for neurological disorders using speech and language outcomes.

Existing systematic reviews have largely focused on classification performance or the architecture of ML models for AD detection. However, far fewer reviews have synthesized which specific acoustic, linguistic, and discourse-level markers show the greatest promise for improving participant selection and pre-screening efficiency in AD clinical trials. In this narrative review, we aim to highlight validated speech and language markers associated with early AD, evaluate the tasks used to elicit these markers, and discuss their potential integration into clinical trial screening workflows.

Methodological Approach to Literature Selection

To increase transparency in the selection of studies included in this narrative review, we employed a targeted literature identification approach aligned with the primary aim of identifying acoustic, linguistic, and discourse-level markers that may support participant screening in AD clinical trials. Searches were conducted in PubMed, Scopus, Web of Science, and Google Scholar using combinations of the terms AD, mild cognitive impairment (MCI), preclinical AD, speech, language, acoustic features, linguistic features, discourse, digital biomarkers, natural language processing, automatic speech recognition, and clinical trials. Studies were retained only if they directly informed the review’s aim of identifying interpretable speech and language markers relevant to AD trial screening. Studies were eligible for inclusion if they reported quantitative speech or language markers derived from recorded speech samples; included participants with AD, MCI, or biomarker-confirmed AD; used tasks relevant to cognitive-linguistic functioning (such as picture description, verbal fluency, narrative recall, or conversational speech); and provided interpretable acoustic, lexical, syntactic, semantic, or discourse-level results. Studies were excluded if they focused solely on classifier performance without interpretable features, relied exclusively on written text, or used simulated or nonhuman speech datasets. These criteria yielded 11 studies, summarized in Table 1. Given the known variability introduced by language, task type, and elicitation methods, methodological characteristics were extracted to contextualize heterogeneity across studies. Consistent with the aims and conventions of a narrative review, no quantitative synthesis or meta-analysis was performed. This review was not designed as a systematic review or meta-analysis, as the primary aim was to synthesize interpretable speech and language markers relevant to clinical trial screening rather than to estimate pooled diagnostic accuracy. Given the heterogeneity in tasks, features, and study designs, a narrative synthesis approach was considered more appropriate.

Table 1.

Studies for characterizing speech and language performance in AD

Disease/Study	Objective	Speech component	Cohort	Tasks	Language used	Results
AD [30]	Automated analysis of natural speech for AD co-pathology in Lewy body dementia biologically confirmed cohorts	Lexical-semantic and acoustic features	AD co pathology (SYN+AD) = 22	Cookie Theft picture description task¤	English as a first language	Age of acquisition of nouns (p = 0.034) and lexical density (p = 0.0064) were reduced in SYN + AD
			Without AD co pathology (SYN-AD) = 38			Classification: 95% sensitivity, 66% specificity; AUC = 0.82
AD [31]	To classify AD, MCI, and CN groups by using speech features	70 acoustic features, 11 prosodic features, and 11 linguistic features	CN = 43	Five speech tasks: counting backwards, subtraction, tasks for phonemic and semantic verbal fluency, and picture description with the Cookie Theft picture	Japanese	ML speech classifier accuracy for classifying AD, MCI, and CN = 78.6%
			MCI = 46			AD versus CN: 91.2% accuracy; MCI versus CN: 87.6% accuracy
			AD = 25
AD [32]	Comparison of automatic interviewer vs. human interviewer in detecting AD	Average speech time, variance of speech, skewness of speech time, kurtosis of speech time, index of speech time	AD = 41	One spontaneous speech task (free topic)	Spanish	Automatic interviewers achieved performances close to those of the traditional human interviewer. Human interviewer achieved sensitivity of 94% and specificity of 93%, respectively, compared to 85% and 75% obtained with the automatic interviewer
			HC = 46	Three induced speech and to incite the subjects to speak, a series of selected videos are used as stimulus
AD [33]	To identify clinically relevant language features for mild AD, aMCI and healthy controls using NLP	Lexical, syntactic, and semantic features	HC = 29 aMCI = 32	Semantic word fluency	Community-dwelling English speakers	Classification: HC vs. cognitive decline: Accuracy 87%, AUC = 0.86; HC vs. aMCI: accuracy 87%, AUC = 0.88; cognitively healthy vs. AD: accuracy 88%, AUC = 0.90
			AD = 30	Free speech
AD [34]	Developing a hybrid stacked fusion model to predict AD using language	Lexico-syntactic	21 samples from AD	The Cookie Theft picture description task	English-speaking participants	Stacked fusion model: AUC of 98.1%
			243 samples from HC			Hybrid stacked fusion model: AUC of 99.2%
AD [35]	Exploring the capability of phonemic verbal fluency to predict cognitive decline and its associations with acoustic and lexical features	Acoustic features (mean-speech-duration, silence-to-word-ratio, word rate, phoneme rate) Lexical features (Brunet’s-index, Honoré's statistic)	HC = 147	Phonemic verbal fluency	German language	VFe higher scores in HC than in MCI/AD with p < 0.001
			MCI/AD = 99			VF is associated with word rate (r = 0.14, p < 0.05), phoneme rate (r = 0.14, p < 0.05), mean-speech-duration (r = 0.17, p < 0.01) and the silence-to-word-rate (r = −0.13, p < 0.05)
AD [36]	To study linguistic performance as an early biomarker of AD	Verbosity, lexical richness, repetitiveness, writing performance and style, syntactic and semantic complexity	AD = 40	Cookie theft picture description task	English	AUC of 0.74 and accuracy of 0.70
			HC = 40
AD [37]	To prove that digital speech assessments can be used as quick, naturalistic, remote assessment of language abilities and cognitive status	Acoustic, lexical, semantic, and syntactic features	High MoCA = 18	Picture description, paragraph reading and recall, letter and semantic fluency, and object naming	English	Word-finding difficulty (p < 0.001), syntactic complexity (p = 0.004), information content (p < 0.001), and coherence (p = 0.02) were sensitive to detect differences between cognitively healthy older adults and those with MCI or early AD.
			Low MoCA = 18
			MCI/AD = 14
AD [38]	Measuring the extent in which cognition, speech and language features differed between participants with different neuronal disorders and CN.	Acoustic modelling = 23 features, linguistic modelling = 26 features (lexical feature = 9, POS and syntactic = 12, discourse cohesion = 5)	AD = 87	Cookie theft picture description task	English native speaker	Acoustic features with p < 0.05: F0, speech time, pause length, pause percentage
			CN = 79			Linguistic features with p < 0.05: RTTR, CTTR, MATTR, HD-D, shorted words and sentences, lower number of nouns, adverbs and numerals, reduced number of contrastive connectives
AD [39]	Using lexical-semantic and acoustic measures as Alzheimer’s disease (AD) digital voice biomarkers	Acoustic and lexical-semantic	Cognitively unimpaired = 92; cognitively impaired = 114	Picture description, nonstructured natural speech and speech during the verbal fluency and confrontation naming tasks	English	Lexical-semantic AUC = 0.80
						Acoustic AUC = 0.77
						Boston Naming Test AUC = 0.66
AD [40]	Analyzing accuracy of classifiers using input features automatically extracted from five different speech tasks	Lexico-syntactic (275) Semantic (11)	HC = 25	Description (PD); conversational speech (CS); overlearned narrative recall (ONR); procedural recall (PR); and novel narrative retelling (NNR)	English as first language	Overlearned narrative recall: accuracy 0.78, specificity 0.82, AUC (0.84)
			AD+MCI = 25			Picture description: accuracy 0.76, specificity 0.81, AUC 0.84
						HC vs. AD+MCI classification: PD = 62%, ONR = 78%

AD, Alzheimer's disease; MCI, mild cognitive impairment; CN, cognitively normal; aMCI, amnestic mild cognitive impairment; VF, verbal fluency; MoCA, montreal cognitive assessment; RTTR, root type-token ration; CTTR, corrected type-token ratio; MATTR, moving average type-token ratio; HD-D, hypergeometric distribution D.

While picture description tasks, such as the Cookie Theft scene, have historically shown robust discriminatory power for early AD-related language changes, current recommendations encourage culturally and gender-neutral stimuli to avoid implicit biases.

Speech and Language Markers in AD

Spoken language production reflects integrated cognitive-linguistic and motor processes, making it a sensitive domain for detecting early AD-related changes. Across the literature, alterations have been reported in acoustic [41], lexical, semantic [24, 42], syntactic [43], and discourse-level properties of speech. Below, we synthesize findings from the included studies and recent literature, organized by analytical level. Acoustic and lexico-semantic features associated with AD are summarized in online supplementary Table S1 and Table S2 [44–57] (for all online suppl. material, see https://doi.org/10.1159/000552094).

Acoustic Markers in AD

Acoustic markers in AD have been interpreted as reflecting increased cognitive-linguistic effort rather than early motor-speech impairment, with subtle yet consistent alterations in timing, prosody, and vocal stability. Individuals with AD typically exhibit slower speech rates, longer and more frequent pauses, reduced articulation rate, and prolonged inter-pause units, with pause duration and pause proportion reported to differ between AD, MCI, and healthy aging while correlating with cognitive performance [38, 58–61]. Extended pauses during spontaneous speech are often associated with lexical retrieval difficulty, whereas increased pause counts during verbal fluency may reflect challenges in efficiently accessing semantic networks. Spectral and voice-quality features also show disease-related changes, including shifts in formant frequencies, reduced vowel space area, and increased articulatory timing variability [62–65]. Mel frequency cepstral coefficients, which capture vocal tract resonances, consistently differentiate cognitively impaired from unimpaired groups despite limited interpretability [66–68]. Additionally, reduced fundamental frequency (f0) variability and narrower pitch range have been observed in AD [69], which may reflect diminished prosodic modulation associated with cognitive strain. Collectively, these acoustic markers have been interpreted as consistent with early disruptions in rhythmicity, prosody, and articulatory stability, and have been reported to contribute to differentiation of AD and MCI from healthy aging.

Linguistic Markers in AD

Linguistic impairment is commonly observed in symptomatic AD, with deficits in semantic memory, lexical access, and conceptual organization associated with characteristic changes in language output. Individuals with AD commonly exhibit reduced lexical diversity, impaired noun retrieval, increased pronoun use, diminished semantic specificity, and lower information content, with type-token measures such as root type-token ratio, corrected type-token ratio (CTTR), moving average type-token ratio, hypergeometric distribution D (HD-D) reliably distinguishing AD from healthy controls [24, 36, 38]. Several studies have reported greater impairment in semantic verbal fluency than phonemic verbal fluency in early AD, although findings across studies remain heterogenous and may depend on task design and cohort characteristics. This pattern has been hypothesized to reflect degradation of semantic networks, and performance differences may arise from both linguistic processes (e.g., clustering and switching) and acoustic components such as word rate and silence-to-word ratios [35]. Syntactic and morphosyntactic changes also emerge, with spoken language production often characterized by simplified syntactic structure, shorter utterances, and fewer embedded clauses, while core morphosyntactic mechanisms may appear relatively preserved in the earliest stages but progressively simplify as the disease advances [43, 40].

Discourse-Level Markers in AD

Discourse production places substantial demands on planning, sequencing, monitoring, and semantic integration, making discourse-level markers among the most sensitive indicators of early AD. Individuals with AD frequently exhibit reduced global coherence and difficulty maintaining topics, accompanied by shorter and more fragmented narrative structures [40]. Their discourse often contains lower informativeness and idea density, along with diminished use of cohesive devices and contrastive connectives, which may reflect weakened semantic organization and impaired integration of conceptual information [38]. These disruptions in discourse structure may reflect higher order cognitive decline and have been reported to emerge earlier than deficits captured by more constrained linguistic tasks. Figure 3 shows a diagram of acoustic and lexical origin in Alzheimer’s populations and shows tasks involved in speech and language analysis.

Fig. 3.

Characterization of acoustic and lexico-semantic speech and language features in AD using various tasks. f0, fundamental frequency; HNR, harmonic-to-noise ratio; MFCC, mel frequency cepstral coefficient; LPC, linear predictive coding; P/N, pronoun to noun ratio; P frequency, pronoun frequency. Directionality is not uniform across conditions (for citations and definitions, see online supplementary Tables S1, S2) [70–75].

While numerous studies report associations between speech and language features and AD, these relationships are complex and influenced by task design, cohort characteristics, and methodological differences. As such, proposed mechanisms should be interpreted as hypotheses requiring further validation.

Task-Dependent Variability in the Diagnostic Utility of Speech and Language Measures in AD

Speech and language markers vary substantially depending on the elicitation task, as different tasks recruit distinct cognitive-linguistic and motor subsystems. Picture description tasks, such as the Cookie Theft scene, capture lexical diversity, syntactic complexity, temporal dynamics, and information content and have been reported to show consistent discriminatory pattern between healthy controls, MCI, and AD [36, 38]. Verbal fluency tasks, encompassing both semantic and phonemic fluency, have been shown to be sensitive to early semantic degradation, and acoustic timing features further enhance classification performance. Narrative recall and spontaneous speech tasks reveal deficits in coherence, thematic progression, and broader discourse structure, with classifier accuracy improving when combined with picture description or other naturalistic tasks [34, 40]. In contrast, reading-aloud and highly structured tasks are less sensitive because they constrain lexical and syntactic choices, resulting in smaller group differences. Overall, multitask approaches have been reported to outperform single-task designs by capturing complementary linguistic and acoustic information. Reported classifier accuracies across these tasks typically range between approximately 70% and 90% depending on feature sets, task design, and cohort composition.

Table 1 summarizes key studies that applied digital speech and language analysis to characterize communication changes in AD and MCI. Studies vary in design, cohort composition, and elicitation tasks; however, consistent patterns emerge across acoustic, prosodic, lexical, semantic, and syntactic domains. Picture description tasks, most commonly the Cookie Theft image, were frequently used and have been reported to differentiate cognitively normal individuals from those with MCI or AD through measures such as pause duration, lexical density, information content, and vowel or pitch variability. Verbal fluency tasks revealed declines in word rate, phoneme rate, and speech-silence ratios. Narrative recall and spontaneous speech captured impairments in coherence, verbosity, and syntactic complexity.

Across studies, machine-learning classifiers trained on these features have reported moderate to strong discriminative performance, with accuracy typically ranging from 70 to 90% and AUC values from 0.74 to 0.99 depending on task and feature set. Hybrid models combining acoustic and lexico-semantic features have been reported to outperform single-modality models. Several studies also showed that language features could identify cognitive decline or AD co-pathology with high sensitivity, supporting their potential role in early detection and digital prescreening. Together, the evidence illustrates that speech and language based features, particularly when collected across diverse tasks and analyzed using interpretable machine-learning methods, represent candidate digital biomarkers for cognitive impairment and AD, offering scalable and low-burden tools for screening and clinical trial enrichment.

Application of Speech and Language Analytics for Clinical Trial Screening

Speech and language analytics offer a scalable and efficient complement to existing clinical trial screening procedures for AD. Digital speech and language measures can support pre-screening and triage by identifying individuals who exhibit subtle cognitive-linguistic changes and are more likely to meet downstream biomarker-confirmation criteria [76]. Emerging evidence suggests meaningful associations between acoustic and linguistic markers and biological indicators such as amyloid-β and tau, potentially enabling more targeted referral for confirmatory imaging or fluid testing [39]. Remote speech and language based assessments in large-scale initiatives, including ADNI4, have shown potential utility in identifying at-risk participants prior to in-clinic evaluation [77]. Additionally, natural language features have been shown to predict future progression to AD years before clinical onset, with reported AUCs up to 0.74 [36]. Speech and language analytics can also improve trial quality assurance; advances in large language models enable automated verification of clinician adherence to scripted procedures, reducing variability and enhancing assessment fidelity [78, 79]. Collectively, these capabilities suggest speech and language analytics as an important adjunct, though not a replacement, for biological confirmation within clinical trial pipelines.

Challenges and Limitations in Implementation

Despite substantial promise, several challenges limit the clinical integration of speech and language analytics in AD. Interindividual variability in speech and language, influenced by age, sex, education, language background, cultural norms, and emotional state, complicates generalizability and underscores the need for representative datasets [80]. Variability in recording hardware, microphones, and environmental conditions introduces noise that reduces comparability across studies and settings [81]. Current evidence is further constrained by small, homogeneous datasets and task heterogeneity, which lead to inconsistent feature expression and hinder synthesis of findings. Automatic Speech Recognition (ASR) systems, although improving with modern models such as Whisper and wav2vec2, continue to show elevated error rates in cognitively impaired populations, affecting transcription quality and downstream natural language processing; clinically meaningful integration will require disorder-specific fine-tuning and rigorous validation [82, 83]. Privacy, equity, and usability concerns also remain central, as voice data constitute sensitive information and digital literacy varies widely. Ensuring equitable model performance across languages, dialects, and demographic groups is essential to avoid bias [84]. Future progress will depend on larger and more diverse datasets, harmonized protocols, transparent sharing of datasets and code, and multimodal approaches that support clinically validated and generalized speech-based pipelines.

Conclusion

This review is intended as a focused narrative synthesis aimed at clinical trialists and digital biomarker researchers, with emphasis on interpretable speech and language features relevant to screening workflows. It does not aim to provide exhaustive coverage or pooled diagnostic estimates, but rather to highlight features with potential translational relevance.

Speech and language features represent a promising set of candidate markers that may support the efficiency and accessibility of AD clinical trial screening. Across the literature, several domains have been consistently reported to show potential for identifying early cognitive impairment and supporting cohort enrichment.

Among acoustic features, temporal measures such as pause duration, pause proportion, articulation rate, and inter-pause intervals have been identified as among the most robust and replicable markers. These measures correlate with cognitive performance and have been reported to differentiate cognitively healthy individuals from those with MCI or early symptomatic AD. Spectral features including Mel frequency cepstral coefficients and vowel space metrics have also been reported to show discriminative value, although their clinical interpretability remains limited.

Linguistic markers have been reported to demonstrate comparable or greater sensitivity to early AD. Reductions in lexical diversity, information content, and noun production, as well as impaired semantic specificity and increased reliance on non-content words have been consistently reported as characteristic features. Syntactic simplification and reduced discourse coherence, particularly during spontaneous or semi-structured speech, have been identified as informative indicators of early decline.

The diagnostic performance of these markers is highly task-dependent, with picture description, narrative recall, and spontaneous speech tasks yielding the richest and complementary feature sets. Multitask approaches have been reported to outperform single-task assessments by capturing distinct cognitive-linguistic and acoustic processes.

In the context of clinical trial workflows, speech and language analytics may function most effectively as a prescreening and stratification tool rather than a standalone diagnostic method. They may help prioritize individuals for confirmatory biomarker testing, reduce screen failure rates, and improve cohort representativeness. Emerging evidence suggests potential associations between speech and language features and biological indicators such as amyloid and tau; however, these relationships remain to be fully established.

Despite these advantages, limitations persist. Demographic variability, heterogeneous data collection methods, persistent ASR challenges, and small sample sizes constrain current generalizability. Addressing these challenges will require larger, more diverse datasets, harmonized task protocols, improved ASR models adapted to cognitive disorders, and rigorous clinical validation.

In summary, the most consistently reported speech and language markers for AD trial screening include temporal acoustic features (particularly pause-based metrics), lexical-semantic measures (lexical richness, semantic specificity, information content), and discourse-level markers (coherence and narrative structure). When integrated into scalable, remote-capable digital pipelines, these features may support more efficient and inclusive AD clinical trial recruitment, although further validation is required before routine clinical implementation.

Conflict of Interest Statement

Prof Vogel, Dr Kathiresan, and Dr Siddiqui are employees of Redenlab Ltd.

Funding Sources

This research was funded by the University of Melbourne Graduate Research Scholarship, awarded to support graduate students conducting advanced research at the University.

Author Contributions

A.S. drafted the manuscript and performed revisions under the supervision and guidance of A.P.V., who directed the study and critically revised the work. T.K. contributed to writing. J.A., P.J.S., A.C., and M.O. provided critical review and feedback. All authors approved the final manuscript.

Funding Statement

This research was funded by the University of Melbourne Graduate Research Scholarship, awarded to support graduate students conducting advanced research at the University.