Voice Patterns in Schizophrenia: A systematic Review and Bayesian Meta-Analysis

Abstract

Voice atypicalities have been a characteristic feature of schizophrenia since its first definitions. They are often associated with core negative symptoms such as flat affect and alogia, and with the social impairments seen in the disorder. This suggests that voice atypicalities may represent a marker of clinical features and social functioning in schizophrenia. We systematically reviewed and meta-analyzed the evidence for distinctive acoustic patterns in schizophrenia, as well as their relation to clinical features. We identified 46 articles, including 55 studies with a total of 1254 patients with schizophrenia and 699 healthy controls. Summary effect sizes (Hedges'g and Pearson's r) estimates were calculated using multilevel Bayesian modeling. We identified weak atypicalities in pitch variability (g = -0.55) related to flat affect, and stronger atypicalities in proportion of spoken time, speech rate, and pauses (g's between -0.75 and -1.89) related to alogia and flat affect. However, the effects were mostly modest (with the important exception of pause duration) compared to perceptual and clinical judgments, and characterized by large heterogeneity between studies.Moderator analyses revealed that tasks with a more demanding cognitive and social component showed larger effects both in contrasting patients and controls and in assessing symptomatology. In conclusion, studies of acoustic patterns are a promising but, yet unsystematic avenue for establishing markers of schizophrenia. We outline recommendations towards more cumulative, open, and theory-driven research.

The Ability of Patients With Schizophrenia to Comprehend and Produce Sincere, Deceitful, and Ironic Communicative Intentions: The Role of Theory of Mind and Executive Functions

“…Typically, the studies in the literature have focused on the ability of patients with schizophrenia to use language (e.g., Linscott, 2005; Moro et al, 2015; Thanh et al, 2017; Corcoran et al, 2018; Pauselli et al, 2018), while non-verbal, extralinguistic and paralinguistic modalities have been generally overlooked (see Cohen et al, 2014; Parola et al, 2018, 2019b). However, Colle et al (2013) assessed the ability of individuals with schizophrenia to comprehend and produce different types of pragmatic phenomena, such as direct and indirect communicative acts, deceit and irony, and investigated extralinguistic and paralinguistic modalities in addition to language.…”

Facial and vocal markers of schizophrenia measured using remote smartphone assessments

“…Despite the exploratory nature of this study and given the small data sample, we attempted to be parsimonious in selection of markers to reduce the likelihood of false discovery. Analysis of vocal markers included those that have previously demonstrated effects in studies of individuals with schizophrenia (Alberto et al, 2019; Martínez-Sánchez et al, 2015). These properties, recorded both during free speech and evoked vocal expressions, include loudness of the individual’s voice in Decibels ( vocal intensity ), average fundamental frequency in Hertz ( fundamental frequency mean ), standard deviation of fundamental frequency ( fundamental frequency stdev ), jitter ( vocal jitter ), harmonics-to-noise ratio ( harmonics to noise ratio ) and the percentage of time with detected speech in an audio file ( speech prevalence) (Cannizzaro et al, 2005; Covington et al, 2012; Kliper et al, 2019; Sarioglu Kayi et al, 2017; Saxman & Burk, 1968).…”

“…Free speech and spontaneous expressivity: Participants were shown images from the Open Affective Standard Image Set (Kurdi et al, 2017) and asked to describe the images and talk about how they made them feel ( Figure 1b). The participants' speech and facial expressivity in response to the prompts were captured (Alberto et al, 2019;Cohen et al, 2016;Kohler, Martin, Milonova, et al, 2008;Kohler, Martin, Stolar, et al, 2008;Mandal et al, 1998;Mattes et al, 1995;Schwartz et al, 2006). This assessment was conducted on days 2, 7, and 14 of the study.…”

“…A number of behavioral characteristics of schizophrenia, such as alogia (poverty of speech) and affective flattening (diminished emotional expression/emotional withdrawal; (Tandon et al, 2013) can be quantified directly using standardized tasks and coding schemes (Alberto et al, 2019;de Boer et al, 2020;Kohler, Martin, Milonova, et al, 2008;Mandal et al, 1998;Mattes et al, 1995), which can be automated through use of computer vision (Baltrusaitis et al, 2016) and vocal acoustic (Jadoul et al, 2018) machine learning models. In addition to digital measures that are directly analogous to core schizophrenia symptomatology, there are a number of other acoustic measures including vocal loudness, pitch variability, fundamental frequency, and jitter that have demonstrated validity as markers of schizophrenia (Alberto et al, 2019;Covington et al, 2012;Martínez-Sánchez et al, 2015;Saxman & Burk, 1968). These markers have demonstrated specificity as measures of the negative symptom cluster in particular (Covington et al, 2012).…”

Vocal acoustic analysis and machine learning for the identification of schizophrenia

“…It is characterized by hallucinations, delusions, thought and behavior disorder or catatonia, and "negative symptoms," such as diminished emotional expression and avolition (American Psychiatric Association 2013). Since the first descriptions of this disorder, speech/ language deficits have been described as remarkable features of schizophrenia, and are often associated with core negative symptoms and social impairment (Alberto et al 2019). These symptoms comprise poverty of speech, disorganized speech, derailment, tangentiality, neologism, incoherence, mutism, perseveration, echolalia, thought blocking (Mac-Kay et al 2018) inappropriate affect prosody or aprosodia (Chakraborty et al 2018a;Covington et al 2012;Elite et al 2014).…”