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Alzheimer’s disease (AD) is a pervasive neurodegenerative disease that affects millions worldwide and is most prominently associated with broad cognitive decline, including language impairment. Picture description tasks are routinely used to monitor language impairment in AD. Due to the high amount of manual resources needed for an in-depth analysis of thereby-produced spontaneous speech, advanced natural language processing (NLP) combined with machine learning (ML) represents a promising opportunity. In this applied research field though, NLP and ML methodology do not necessarily ensure robust clinically actionable insights into cognitive language impairment in AD and additional precautions must be taken to ensure clinical-validity and generalizability of results. In this study, we add generalizability through multilingual feature statistics to computational approaches for the detection of language impairment in AD. We include 154 participants (78 healthy subjects, 76 patients with AD) from two different languages (106 English speaking and 47 French speaking). Each participant completed a picture description task, in addition to a battery of neuropsychological tests. Each response was recorded and manually transcribed. From this, task-specific, semantic, syntactic and paralinguistic features are extracted using NLP resources. Using inferential statistics, we determined language features, excluding task specific features, that are significant in both languages and therefore represent “generalizable” signs for cognitive language impairment in AD. In a second step, we evaluated all features as well as the generalizable ones for English, French and both languages in a binary discrimination ML scenario (AD vs. healthy) using a variety of classifiers. The generalizable language feature set outperforms the all language feature set in English, French and the multilingual scenarios. Semantic features are the most generalizable while paralinguistic features show no overlap between languages. The multilingual model shows an equal distribution of error in both English and French. By leveraging multilingual statistics combined with a theory-driven approach, we identify AD-related language impairment that generalizes beyond a single corpus or language to model language impairment as a clinically-relevant cognitive symptom. We find a primary impairment in semantics in addition to mild syntactic impairment, possibly confounded by additional impaired cognitive functions.

Background Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project aimed to identify discriminating speech features between patients with major depressive disorder (MDD) and healthy controls (HCs) by examining associations with symptom severity measures. Methods Forty-four MDD patients from the Psychiatry Department, University Hospital Aachen, Germany and fifty-two HCs were recruited. Participants described positive and negative life events, which were recorded for analysis. The Beck Depression Inventory (BDI-II) and the Hamilton Rating Scale for Depression gauged depression severity. Transcribed audio recordings underwent feature extraction, including acoustics, speech rate, and content. Machine learning models including speech features and neuropsychological assessments, were used to differentiate between the MDD patients and HCs. Results Acoustic variables such as pitch and loudness differed significantly between the MDD patients and HCs (effect sizes 𝜼2 between 0.183 and 0.3, p  < 0.001). Furthermore, variables pertaining to temporality, lexical richness, and speech sentiment displayed moderate to high effect sizes (𝜼2 between 0.062 and 0.143, p  < 0.02). A support vector machine (SVM) model based on 10 acoustic features showed a high performance (AUC = 0.93) in differentiating between HCs and patients with MDD, comparable to an SVM based on the BDI-II (AUC = 0.99, p  = 0.01). Conclusions This study identified robust speech features associated with MDD. A machine learning model based on speech features yielded similar results to an established pen-and-paper depression assessment. In the future, these findings may shape voice-based biomarkers, enhancing clinical diagnosis and MDD monitoring.

Certain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders. Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers. Different speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality-and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores. Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.

Background Automated speech analysis has gained increasing attention to help diagnosing depression. Most previous studies, however, focused on comparing speech in patients with major depressive disorder to that in healthy volunteers. An alternative may be to associate speech with depressive symptoms in a non-clinical sample as this may help to find early and sensitive markers in those at risk of depression. Methods We included n =  118 healthy young adults (mean age: 23.5 ± 3.7 years; 77% women) and asked them to talk about a positive and a negative event in their life. Then, we assessed the level of depressive symptoms with a self-report questionnaire, with scores ranging from 0–60. We transcribed speech data and extracted acoustic as well as linguistic features. Then, we tested whether individuals below or above the cut-off of clinically relevant depressive symptoms differed in speech features. Next, we predicted whether someone would be below or above that cut-off as well as the individual scores on the depression questionnaire. Since depression is associated with cognitive slowing or attentional deficits, we finally correlated depression scores with performance in the Trail Making Test. Results In our sample, n =  93 individuals scored below and n =  25 scored above cut-off for clinically relevant depressive symptoms. Most speech features did not differ significantly between both groups, but individuals above cut-off spoke more than those below that cut-off in the positive and the negative story. In addition, higher depression scores in that group were associated with slower completion time of the Trail Making Test. We were able to predict with 93% accuracy who would be below or above cut-off. In addition, we were able to predict the individual depression scores with low mean absolute error (3.90), with best performance achieved by a support vector machine. Conclusions Our results indicate that even in a sample without a clinical diagnosis of depression, changes in speech relate to higher depression scores. This should be investigated in more detail in the future. In a longitudinal study, it may be tested whether speech features found in our study represent early and sensitive markers for subsequent depression in individuals at risk.

INTRODUCTION Practice effects are an improvement in task performance with repeated testing. Their absence may indicate compromised learning and may help discriminate healthy from pathological ageing. METHODS We recorded semantic verbal fluency three times in n = 58 healthy older adults or patients with amnestic mild cognitive impairment (MCI) (72.16 ± 4.83 years old, 33 women). We extracted speech features and trained a machine learning classifier on them at each cognitive assessment. We examined which variables were informative for classification and whether they correlated with episodic memory performance. RESULTS We found smaller practice effects in patients with amnestic MCI. There was a 13% improvement in classification performance with features from the third cognitive assessment as compared to the first assessment. Practice effects correlated with episodic memory performance in healthy adults. DISCUSSION Speech features became more informative for classification when repeatedly assessed. They may be a promising tool for identifying individuals at risk of cognitive decline. Highlights In MCI, practice effects in verbal fluency tasks were smaller than in healthy adults. Smaller practice effects in MCI indicated compromised learning. Including practice effects improved the classification of MCI vs. healthy ageing. In MCI, practice effects were independent of episodic memory performance.

INTRODUCTION Neuropsychiatric symptoms (NPSs) are early hallmarks of neurocognitive disorders (NCDs). Speech alterations might indicate both cognitive and behavioral changes in NCDs, aiding in diagnosis and disease monitoring. This study examined associations between automatically extracted speech/language features and NPS severity. METHODS A total of N = 37 subjective cognitive decline and N = 20 mild cognitive impairment participants from the BioBank Alzheimer Centre Limburg study were recorded performing a low‐constraint free‐speech task. NPSs were assessed using the Geriatric Depression Scale and the Neuropsychiatric Inventory. Acoustic and linguistic features were automatically extracted. Correlation analysis was performed (adjusted for age, sex, and Mini‐Mental State Examination) between the features and clinical scales. RESULTS Features correlated significantly with NPSs. Indicatively, depression correlated with local jitter (r = 0.38, p < 0.001) and agitation with the sum of pause duration (r = 0.32, p < 0.027). DISCUSSION Speech analysis offers a promising tool for evaluating NPSs in NCDs. Highlights We found links between neuropsychiatric symptom (NPS) severity and speech markers in memory clinic patients. Temporal markers positively correlated with the presence and severity of agitation. Depression was positively correlated with voice instabilities. Anxiety was negatively associated with metrics of lexical diversity. Speech analysis provides an objective tool to assess NPSs in subjective cognitive decline and mild cognitive impairment patients.

Objective:Cortisol is a well-established biomarker of stress, assessed through salivary or blood samples, which are intrusive and time-consuming. Speech, influenced by physiological stress responses, offers a promising non-invasive, real-time alternative for stress detection. This study examined relationships between speech features, state anger, and salivary cortisol using a validated stress-induction paradigm.Methods:Participants (N = 82) were assigned to cold (n = 43) or warm water (n = 39) groups. Saliva samples and speech recordings were collected before and 20 minutes after the Socially Evaluated Cold Pressor Test (SECPT), alongside State–Trait Anger Expression Inventory (STAXI) ratings. Acoustic features from frequency, energy, spectral, and temporal domains were analysed. Statistical analyses included Wilcoxon tests, correlations, linear mixed models (LMMs), and machine learning (ML) models, adjusting for covariates.Results:Post-intervention, the cold group showed significantly higher cortisol and state anger. Stress-related speech changes occurred across domains. Alpha ratio decreased and MFCC3 increased post-stress in the cold group, associated with cortisol and robust to sex and baseline levels. Cortisol–speech correlations were significant in the cold group, including sex-specific patterns. LMMs indicated baseline cortisol influenced feature changes, differing by sex. ML models modestly predicted SECPT group membership (AUC = 0.55) and showed moderate accuracy estimating cortisol and STAXI scores, with mean absolute errors corresponding to ∼ 24–38% and ∼16–28% of observed ranges, respectively.Conclusion:This study demonstrates the potential of speech features as objective stress markers, revealing associations with cortisol and state anger. Speech analysis may offer a valuable, non-invasive tool for assessing stress responses, with notable sex differences in vocal biomarkers.

Multiple sclerosis (MS) is a chronic neuroinflammatory disease characterized by central nervous system demyelination and axonal degeneration. Fatigue affects a major portion of MS patients, significantly impairing their daily activities and quality of life. Despite its prevalence, the mechanisms underlying fatigue in MS are poorly understood, and measuring fatigue remains a challenging task. This study evaluates the efficacy of automated speech analysis in detecting fatigue in MS patients. MS patients underwent a detailed clinical assessment and performed a comprehensive speech protocol. Using features from three different free speech tasks and a proprietary cognition score, our support vector machine model achieved an AUC on the ROC of 0.74 in detecting fatigue. Using only free speech features evoked from a picture description task we obtained an AUC of 0.68. This indicates that specific free speech patterns can be useful in detecting fatigue. Moreover, cognitive fatigue was significantly associated with lower speech ratio in free speech ( ρ  = −0.283, p  = 0.001), suggesting that it may represent a specific marker of fatigue in MS patients. Together, our results show that automated speech analysis, of a single narrative free speech task, offers an objective, ecologically valid and low-burden method for fatigue assessment. Speech analysis tools offer promising potential applications in clinical practice for improving disease monitoring and management.

It is still under debate whether levodopa treatment improves speech functions in Parkinson’s disease (PD). Therefore, speech functions of people with PD were compared in medication-OFF condition (withdrawal of PD medication for at least 12 h) and medication-ON condition (after receiving 200 mg of soluble levodopa). A total of 78 participants, including 51 males and 27 females, performed predefined standard speech tasks. Acoustic speech features were automatically extracted with the algorithm given by the Dysarthria Analyzer. Results suggest that acute levodopa intake improves phonatory-respiratory speech functions and speech planning abilities, while the articulatory system remains unaffected. Furthermore, the study provided preliminary evidence that speech function is able to predict the medication status in individuals with PD as the constructed speech-based biomarker score did not only correlate with established measures of (speech) motor impairment but could also differentiate between the medication OFF and ON status. A post-hoc machine learning model yielded similar results.

Dysarthria, a motor speech disorder caused by muscle weakness or paralysis, severely impacts speech intelligibility and quality of life. The condition is prevalent in motor speech disorders such as Parkinson's disease (PD), atypical parkinsonism such as progressive supranuclear palsy (PSP), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). Improving intelligibility is not only an outcome that matters to patients but can also play a critical role as an endpoint in clinical research and drug development. This study validates a digital measure for speech intelligibility, the ki: SB-M intelligibility score, across various motor speech disorders and languages following the Digital Medicine Society (DiMe) V3 framework. The study used four datasets: healthy controls (HCs) and patients with PD, HD, PSP, and ALS from Czech, Colombian, and German populations. Participants' speech intelligibility was assessed using the ki: SB-M intelligibility score, which is derived from automatic speech recognition (ASR) systems. Verification with inter-ASR reliability and temporal consistency, analytical validation with correlations to gold standard clinical dysarthria scores in each disease, and clinical validation with group comparisons between HCs and patients were performed. Verification showed good to excellent inter-rater reliability between ASR systems and fair to good consistency. Analytical validation revealed significant correlations between the SB-M intelligibility score and established clinical measures for speech impairments across all patient groups and languages. Clinical validation demonstrated significant differences in intelligibility scores between pathological groups and healthy controls, indicating the measure's discriminative capability. The ki: SB-M intelligibility score is a reliable, valid, and clinically relevant tool for assessing speech intelligibility in motor speech disorders. It holds promise for improving clinical trials through automated, objective, and scalable assessments. Future studies should explore its utility in monitoring disease progression and therapeutic efficacy as well as add data from further dysarthrias to the validation.