Analyzing acoustic and prosodic fluctuations in free speech to predict psychosis onset in high-risk youths

Agurto, Carla; Pietrowicz, Mary; Norel, Raquel; Eyigöz, Elif; Stanislawski, Emma; Cecchi, Guillermo A.; Corcoran, Cheryl

doi:10.1109/embc44109.2020.9176841

Cited by 15 publications

(25 citation statements)

References 20 publications

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“…Second, speech disturbances are closely related to important predictors for clinical endpoints. Abnormalities in pausing have been associated with positive and negative symptoms, in both individuals at clinical high risk (Agurto et al, 2020; Sichlinger et al, 2019; Stanislawski et al, 2021) and patients with schizophrenia (Cohen et al, 2016). In addition, speech disturbances are related to cognitive function (Barker et al, 2020; R.…”

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confidence: 99%

Characterizing speech heterogeneity in schizophrenia-spectrum disorders.

Oomen¹,

Boer²,

Brederoo³

et al. 2022

Journal of Psychopathology and Clinical Science

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Schizophrenia-spectrum disorders (SSD) are highly heterogeneous in risk factors, symptom characteristics, and disease course outcome. Although speech anomalies have long been recognized as a core symptom of SSD, speech markers are an unexplored source of symptom heterogeneity that may be informative in recognizing relevant subtypes. This study investigated speech heterogeneity and its relation to clinical characteristics in a large sample of patients with SSD and healthy controls. Speech samples were obtained from 142 patients with SSD and 147 healthy controls by means of open-ended interviews. Speech was analyzed using standardized open-source acoustic speech software. Hierarchical clustering was conducted using acoustic speech markers. Symptom severity was rated with the Positive and Negative Syndrome Scale, and cognition was assessed with the Brief Assessment of Cognition for Schizophrenia. Three speech clusters could be distinguished in the patient group that differed regarding speech properties, independent of medication use. One cluster was characterized by mild speech disturbances, while two severely impaired clusters were recognized (fragmented speakers and prolonged pausers). Both clusters with severely impaired speech had more severe cognitive dysfunction than the mildly impaired speakers. Prolonged pausers specifically had difficulties with memory-related tasks. Prolonged pausing, as opposed to fragmented speaking, related to chronic active psychosis and refractory psychotic symptoms. Based on speech clustering, subtypes of patients emerged with distinct disease trajectories, symptomatology, and cognitive functioning. The identification of clinically relevant subgroups within SSD may help to characterize distinct profiles and benefit the tailoring of early intervention and improvement of long-term functional outcome. General Scientific SummarySpeech anomalies have long been recognized as a core symptom of schizophrenia-spectrum disorders (SSD), yet speech markers are an unexplored source of symptom heterogeneity that may be informative in recognizing relevant subtypes of SSD. This study showed the existence of distinct speech subtypes with divergent disease trajectories, symptomatology, and cognitive functioning. This supports the notion that speech can provide valuable information about the patient and benefits the tailoring of early intervention and improvement of long-term functional outcome.

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confidence: 99%

Characterizing speech heterogeneity in schizophrenia-spectrum disorders.

Oomen¹,

Boer²,

Brederoo³

et al. 2022

Journal of Psychopathology and Clinical Science

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“…The strength of the LMBAS is that it is a theoretically-driven, knowledge-based system that allows users to evaluate the link between speech production and perception. For future work, the performance of LMBAS can be compared to the performance of other acoustic features commonly extracted for use in voice quality analysis, and other neuropsychiatric disorders that commonly affect voice and emotion recognition (Aguiar et al, 2019; Agurto et al, 2019; Agurto et al, 2020; Bone et al, 2017; Cummins et al, 2015; Deshpande et al, 2020; Eyben et al, 2010; Harati et al, 2018; Huang et al, 2018; Konig et al, 2015; Low et al, 2020; Maor et al, 2020; Marmar et al, 2019; Norel et al, 2018; Orozco-Arroyave et al, 2016; Perez et al, 2018; Pinkas et al, 2020; Rusz et al, 2011; Sara et al, 2020). Some of these features include autocorrelation, zero crossing rate, entropy/entropy ratios across targeted spectral ranges, energy/intensity, Mel/Bark Frequency Cepstral Coefficients (MFCC), linear predictive coefficients (LPC), perceptual linear predictive coefficients (PLP), perceptual linear predictive Cepstral Coefficients (PLP-CC), spectral features, psychoacoustic sharpness, spectral harmonicity, F0, F0 Harmonics ratios, jitter/shimmer, and a variety of statistical and mathematical summary measurements for these frame-level values.…”

Section: Discussionmentioning

confidence: 99%

Landmark-based analysis of speech differentiates conversational from clear speech in speakers with muscle tension dysphonia

Ishikawa

Pietrowicz

Charney

et al. 2022

Preprint

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This study evaluated the feasibility of differentiating conversational and clear speech produced by individuals with muscle tension dysphonia (MTD) using Landmark-Based Analysis of Speech (LMBAS). Thirty-four adult speakers with MTD recorded conversational and clear speech. Of those, 27 speakers were able to produce clear speech. The recordings of these individuals were analyzed with an open-source LMBAS program, SpeechMark®, MATLAB Toolbox ver. 1.1.2.The mixed-effect logistic regression model indicated that the burst-onset landmark differentiated conversational speech from clear speech. The LMBAS is a potential approach for detecting the difference between conversational and clear speech in individuals with mild to moderate MTD.

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“…A previous study found that acoustic features were associated with negative symptoms in CHR‐P participants (Stanislawski et al, 2021). Our study and Sichlinger et al (2019) elicited speech from clinical interviews, whilst Agurto et al (2020) and Stanislawski et al (2021) employed open‐ended interviews. The different acquisition protocols might explain the divergent findings, given that the context and content of interview settings significantly influence speech parameters (Cohen et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…The features extracted were grouped into temporal (de Boer et al, 2020) and prosodic (Agurto et al, 2020) measures.…”

Section: Speech Acquisitionmentioning

confidence: 99%

Investigating temporal and prosodic markers in clinical high‐risk for psychosis participants using automated acoustic analysis

Bianciardi

Gajwani

Groß³

et al. 2022

Early Intervention Psych

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Aim: Language disturbances are a candidate biomarker for the early detection of psychosis. Temporal and prosodic abnormalities have been observed in schizophrenia patients, while there is conflicting evidence whether such deficits are present in participants meeting clinical high-risk for psychosis (CHR-P) criteria.Methods: Clinical interviews from CHR-P participants (n = 50) were examined for temporal and prosodic metrics and compared against a group of healthy controls (n = 17) and participants with affective disorders and substance abuse (n = 23).Results: There were no deficits in acoustic variables in the CHR-P group, while participants with affective disorders/substance abuse were characterized by slower speech rate, longer pauses and higher unvoiced frames percentage. Conclusion:Our finding suggests that temporal and prosodic aspects of speech are not impaired in early-stage psychosis. Further studies are required to clarify whether such abnormalities are present in sub-groups of CHR-P participants with elevated psychosis-risk.

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Analyzing acoustic and prosodic fluctuations in free speech to predict psychosis onset in high-risk youths

Cited by 15 publications

References 20 publications

Characterizing speech heterogeneity in schizophrenia-spectrum disorders.

Characterizing speech heterogeneity in schizophrenia-spectrum disorders.

Landmark-based analysis of speech differentiates conversational from clear speech in speakers with muscle tension dysphonia

Investigating temporal and prosodic markers in clinical high‐risk for psychosis participants using automated acoustic analysis

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