Understanding emotional expression using prosodic analysis of natural speech: refining the methodology

Alogia and pressured speech do not fall on a continuum of speech production using objective speech technologies

Speech production is affected in a variety of serious mental illnesses (SMI; e.g., schizophrenia, unipolar depression, bipolar disorders) and at its extremes can be observed in the gross reduction of speech (e.g., alogia) or increase of speech (e.g., pressured speech). The present study evaluated whether clinically-rated alogia and pressured speech represent antithetical constructs when analyzed using objective metrics of speech production. We examined natural speech using acoustic and natural language processing features from two archival studies using several different speaking tasks and a combined 107 patients meeting criteria for SMI. Contrary to expectations, we did not find that alogia and pressured speech presented as opposing ends of a speech production continuum. Objective speech markers were associated with clinically rated alogia but not pressured speech, and these results were consistent across speaking tasks and studies. Implications for our understanding of speech production symptoms in SMI are discussed, as well as implications for Natural Language Processing and digital phenotyping efforts more generally.

A Tutorial Review on Clinical Acoustic Markers in Speech Science

PURPOSE

The human voice changes with the progression of neurological disease and the onset of diseases that affect articulators, often decreasing the effectiveness of communication. These changes can be objectively measured using signal processing techniques that extract acoustic features. When measuring acoustic features, there are often several steps and assumptions that might be known to experts in acoustics and phonetics, but are less transparent for other disciplines (e.g., clinical medicine, speech pathology, engineering, and data science). This tutorial describes these signal processing techniques, explicitly outlines the underlying steps for accurate measurement, and discusses the implications of clinical acoustic markers.

CONCLUSIONS

We establish a vocabulary using straightforward terms, provide visualizations to achieve common ground, and guide understanding for those outside the domains of acoustics and auditory signal processing. Where possible, we highlight the best practices for measuring clinical acoustic markers and suggest resources for obtaining and further understanding these measures.

Ambulatory audio and video recording for digital phenotyping in schizophrenia: Adherence & data usability

Ambulatory audio and video recording provides a wealth of information which can be used for a broad range of applications, including digital phenotyping, telepsychiatry, and telepsychology. However, these technologies are in their infancy, and guidelines for their use and analysis have yet to be established. The current project used ambulatory assessment data from individuals with schizophrenia (N = 52) and controls (N = 55) over a week to assess factors influencing sufficiency and useability of video and audio data. Logistic multilevel models examined the effect of relevant variables on video provision and video quality. There was no difference by group in video provision or quality. Videos were less likely to be provided later in the study and later in the day. Video quality was lower later in the day, particularly for controls. Participants were more likely to provide videos if alone or at home than in other settings. Black participants were less likely to have analyzable video frames than White participants. These results suggest potential racial disparities in camera technologies and/or facial analysis algorithms. Implications of these findings and recommendations for future study development, such as instructions to provide to participants to optimize video quality, are discussed.

Validating Biobehavioral Technologies for Use in Clinical Psychiatry

The last decade has witnessed the development of sophisticated biobehavioral and genetic, ambulatory, and other measures that promise unprecedented insight into psychiatric disorders. As yet, clinical sciences have struggled with implementing these objective measures and they have yet to move beyond "proof of concept." In part, this struggle reflects a traditional, and conceptually flawed, application of traditional psychometrics (i.e., reliability and validity) for evaluating them. This paper focuses on "resolution," concerning the degree to which changes in a signal can be detected and quantified, which is central to measurement evaluation in informatics, engineering, computational and biomedical sciences. We define and discuss resolution in terms of traditional reliability and validity evaluation for psychiatric measures, then highlight its importance in a study using acoustic features to predict self-injurious thoughts/behaviors (SITB). This study involved tracking natural language and self-reported symptoms in 124 psychiatric patients: (a) over 5-14 recording sessions, collected using a smart phone application, and (b) during a clinical interview. Importantly, the scope of these measures varied as a function of time (minutes, weeks) and spatial setting (i.e., smart phone vs. interview). Regarding reliability, acoustic features were temporally unstable until we specified the level of temporal/spatial resolution. Regarding validity, accuracy based on machine learning of acoustic features predicting SITB varied as a function of resolution. High accuracy was achieved (i.e., ~87%), but only when the acoustic and SITB measures were "temporally-matched" in resolution was the model generalizable to new data. Unlocking the potential of biobehavioral technologies for clinical psychiatry will require careful consideration of resolution.

Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia

Negative symptoms are a transdiagnostic feature of serious mental illness (SMI) that can be potentially “digitally phenotyped” using objective vocal analysis. In prior studies, vocal measures show low convergence with clinical ratings, potentially because analysis has used small, constrained acoustic feature sets. We sought to evaluate (1) whether clinically rated blunted vocal affect (BvA)/alogia could be accurately modelled using machine learning (ML) with a large feature set from two separate tasks (i.e., a 20-s “picture” and a 60-s “free-recall” task), (2) whether “Predicted” BvA/alogia (computed from the ML model) are associated with demographics, diagnosis, psychiatric symptoms, and cognitive/social functioning, and (3) which key vocal features are central to BvA/Alogia ratings. Accuracy was high (>90%) and was improved when computed separately by speaking task. ML scores were associated with poor cognitive performance and social functioning and were higher in patients with schizophrenia versus depression or mania diagnoses. However, the features identified as most predictive of BvA/Alogia were generally not considered critical to their operational definitions. Implications for validating and implementing digital phenotyping to reduce SMI burden are discussed.

Ambulatory digital phenotyping of blunted affect and alogia using objective facial and vocal analysis: Proof of concept

Negative symptoms reflect one of the most debilitating aspects of one of the most debilitating diseases known to humankind. As yet, our treatments for negative symptoms are palliative at best and our understanding of their causes is relatively superficial. To address this, we are developing objective ambulatory tools for digitally phenotyping their severity which can be used outside the confines of the traditional clinical and research settings. The present study evaluated the feasibility, reliability and validity of ambulatory vocal acoustic and facial emotion expression analysis. Videos were provided by 25 patients with schizophrenia or schizoaffective disorder and 27 nonpsychiatric controls using inexpensive, non-invasive ambulatory recording methods. Controls provided 411 video recordings, and patients provided 377 video recordings; an average of 15.22 and 14.50 per participant per group respectively. The vast majority (over 80%) of these videos were usable for analysis. An empirically-supported, limited-feature vocal (7 features) and facial (3 features) set was examined. Within participants, these features varied considerably over time, but showed moderate to good test-retest reliability in many cases once contextual factors (e.g., activity involved in at the time of testing) were accounted for. Vocal and facial features showed statistically significant convergence with a "gold standard" negative symptom measure. Ambulatory vocal/facial features were more strongly associated with engagement in social or work activities in patients than negative symptom ratings. These data support the use of ambulatory vocal/facial analytic technologies for digital phenotyping of these negative symptoms.

Acoustic differences between healthy and depressed people: a cross-situation study

BACKGROUND

Abnormalities in vocal expression during a depressed episode have frequently been reported in people with depression, but less is known about if these abnormalities only exist in special situations. In addition, the impacts of irrelevant demographic variables on voice were uncontrolled in previous studies. Therefore, this study compares the vocal differences between depressed and healthy people under various situations with irrelevant variables being regarded as covariates.

METHODS

To examine whether the vocal abnormalities in people with depression only exist in special situations, this study compared the vocal differences between healthy people and patients with unipolar depression in 12 situations (speech scenarios). Positive, negative and neutral voice expressions between depressed and healthy people were compared in four tasks. Multiple analysis of covariance (MANCOVA) was used for evaluating the main effects of variable group (depressed vs. healthy) on acoustic features. The significances of acoustic features were evaluated by both statistical significance and magnitude of effect size.

RESULTS

The results of multivariate analysis of covariance showed that significant differences between the two groups were observed in all 12 speech scenarios. Although significant acoustic features were not the same in different scenarios, we found that three acoustic features (loudness, MFCC5 and MFCC7) were consistently different between people with and without depression with large effect magnitude.

CONCLUSIONS

Vocal differences between depressed and healthy people exist in 12 scenarios. Acoustic features including loudness, MFCC5 and MFCC7 have potentials to be indicators for identifying depression via voice analysis. These findings support that depressed people's voices include both situation-specific and cross-situational patterns of acoustic features.

Speech production and disorganization in schizotypy: Investigating the role of cognitive and affective systems

Diminished productivity and elevated disorganization have been detected in the speech of individuals with schizotypy. However, the underlying mechanisms for these disruptions are not well understood. Separate lines of research suggest potential contributions from cognitive and affective systems. In this study, disorganized speech and speech production were examined in speech samples generated by schizotypy (n = 47) and non-schizotypy (n = 51) groups by assessing "reactivity" (i.e., a change in experimental compared with baseline conditions) across baseline, affective, and dual-task (i.e., cognitive) conditions. Relationships with social functioning were also examined within each group. Three key findings emerged: 1) compared to the non-schizotypy group, those with schizotypy exhibited diminished speech production in the affective condition and affective reactivity was observed; 2) the schizotypy group displayed greater levels of disorganized speech in dual-task conditions and cognitive reactivity was observed; and 3) affective reactivity for disorganized speech was linked to worse social functioning within the schizotypy group. This study provides evidence that cognitive and affective systems are uniquely involved in separate characteristics of speech in schizotypy. At this stage, cognitive systems appear to have a specific role in the organization of speech, whereas affective systems are more heavily involved in speech production. Regarding the association between affective reactivity and social functioning, previous research has demonstrated individuals highly reactive to emotional stimuli carry additional risk for conversion to psychosis. Future research identifying a subset with schizotypy who demonstrate affective reactivity could lead to a better understanding of links between schizotypy and future psychosis symptoms.

Comparing static and dynamic predictors of risk for hostility in serious mental illness: Preliminary findings

This study compared static predictors of hostility (e.g. demographics, clinician ratings) to subjective (i.e., self-reported affect on slider scales in response to written questions) and objective (i.e., vocal indicators of arousal from speech samples in a story-retelling task) dynamic predictors using ambulatory assessment over five days in a sample of 25 stable outpatients with diagnoses of a serious mental illness. Multilevel modeling showed that both subjective and objective dynamic predictors were significant, but none of the static predictors were. These results suggest that, in predicting hostility, it is more important to account for state variation than static traits.

A Comprehensive Review of Computational Methods for Automatic Prediction of Schizophrenia With Insight Into Indigenous Populations

Psychiatrists rely on language and speech behavior as one of the main clues in psychiatric diagnosis. Descriptive psychopathology and phenomenology form the basis of a common language used by psychiatrists to describe abnormal mental states. This conventional technique of clinical observation informed early studies on disturbances of thought form, speech, and language observed in psychosis and schizophrenia. These findings resulted in language models that were used as tools in psychosis research that concerned itself with the links between formal thought disorder and language disturbances observed in schizophrenia. The end result was the development of clinical rating scales measuring severity of disturbances in speech, language, and thought form. However, these linguistic measures do not fully capture the richness of human discourse and are time-consuming and subjective when measured against psychometric rating scales. These linguistic measures have not considered the influence of culture on psychopathology. With recent advances in computational sciences, we have seen a re-emergence of novel research using computing methods to analyze free speech for improving prediction and diagnosis of psychosis. Current studies on automated speech analysis examining for semantic incoherence are carried out based on natural language processing and acoustic analysis, which, in some studies, have been combined with machine learning approaches for classification and prediction purposes.

Blunted vocal affect and expression is not associated with schizophrenia: A computerized acoustic analysis of speech under ambiguous conditions

INTRODUCTION

Patients with schizophrenia are consistently rated by clinicians as having high levels of blunted vocal affect and alogia. However, objective technologies have often failed to substantiate these abnormalities. It could be the case that negative symptoms are context-dependent.

OBJECTIVES

The present study examined speech elicited under conditions demonstrated to exacerbate thought disorder.

METHODS

The Rorschach Test was administered to 36 outpatients with schizophrenia and 25 nonpatient controls. Replies to separate "perceptual" and "memory" phases were analyzed using validated acoustic analytic methods.

RESULTS

Compared to nonpatient controls, schizophrenia patients did not display abnormal speech expression on objective measure of blunted vocal affect or alogia. Moreover, clinical ratings of negative symptoms were not significantly correlated with objective measures.

CONCLUSIONS

These findings suggest that in patients with schizophrenia, vocal affect/alogia is generally unremarkable under ambiguous conditions. Clarifying the nature of blunted vocal affect and alogia, and how objective measures correspond to what clinicians attend to when making clinical ratings are important directions for future research.

A psychometric investigation of "macroscopic" speech measures for clinical and psychological science

The analysis of vocal expression is a critical endeavor for psychological and clinical sciences and is an increasingly popular application for computer-human interfaces. Despite this, and despite advances in the efficiency, affordability, and sophistication of vocal analytic technologies, there is considerable variability across studies regarding what aspects of vocal expression are studied. Vocal signals can be quantified in a myriad of ways, and their underlying structure, at least with respect to "macroscopic" measures from extended speech, is presently unclear. To address this issue, we evaluated the psychometric properties-notably, the structural and construct validity-of a systematically defined set of global vocal features. Our analytic strategy focused on (a) identifying redundant variables among this set, (b) employing principal components analysis (PCA) to identify nonoverlapping domains of vocal expression, (c) examining the degrees to which the vocal variables are modulated as a function of changes in speech task, and (d) evaluating the relationship between the vocal variables and cognitive (i.e., verbal fluency) and clinical (i.e., depression, anxiety, and hostility) variables. Spontaneous speech samples from 11 independent studies of young adults (>60 s in length), employing one of three different speaking tasks, were examined (N = 1,350). Confounding variables (i.e., sex, ethnicity) were statistically controlled for. The PCA identified six distinct domains of vocal expression. Collectively, vocal expression (defined in terms of these domains) was modulated as a function of speech task and was related to the cognitive and clinical variables. These findings provide empirically grounded implications for the study of vocal expression in psychological and clinical sciences.

Vocal expression in schizophrenia: Less than meets the ear

Abnormalities in nonverbal communication are a hallmark of schizophrenia. Results from studies using symptom rating scales suggest that these abnormalities are profound (i.e., 3-5 SDs) and occur across virtually every channel of vocal expression. Computerized acoustic analytic technologies, used to overcome practical and psychometric limitations with symptom rating scales, have found much more benign and isolated abnormalities. To better understand vocal deficits in schizophrenia and to advance acoustic analytic technologies for clinical and research applications, we examined archived speech samples from 5 separate studies, each using different speaking tasks (patient N = 309; control N = 117). We sought to: (a) use Principal Component Analysis (PCA) to identify independent vocal expression measures from a large set of variables, (b) quantify how patients with schizophrenia are abnormal with respect to these variables, (c) evaluate the impact of demographic and contextual factors (e.g., study site, speaking task), and (d) examine the relationship between clinically-rated psychiatric symptoms and vocal variables. PCA identified 7 independent markers of vocal expression. Most of these vocal variables varied considerably as a function of context and many were associated with demographic factors. After controlling for context and demographics, there were no meaningful differences in vocal expression between patients and controls. Within patients, vocal variables were associated with a range of psychiatric symptoms-though only pause length was significantly associated with clinically rated negative symptoms. The discussion centers on explaining the apparent discordance between clinical and computerized speech measures.

Recruitment of Language-, Emotion- and Speech-Timing Associated Brain Regions for Expressing Emotional Prosody: Investigation of Functional Neuroanatomy with fMRI

We aimed to progress understanding of prosodic emotion expression by establishing brain regions active when expressing specific emotions, those activated irrespective of the target emotion, and those whose activation intensity varied depending on individual performance. BOLD contrast data were acquired whilst participants spoke non-sense words in happy, angry or neutral tones, or performed jaw-movements. Emotion-specific analyses demonstrated that when expressing angry prosody, activated brain regions included the inferior frontal and superior temporal gyri, the insula, and the basal ganglia. When expressing happy prosody, the activated brain regions also included the superior temporal gyrus, insula, and basal ganglia, with additional activation in the anterior cingulate. Conjunction analysis confirmed that the superior temporal gyrus and basal ganglia were activated regardless of the specific emotion concerned. Nevertheless, disjunctive comparisons between the expression of angry and happy prosody established that anterior cingulate activity was significantly higher for angry prosody than for happy prosody production. Degree of inferior frontal gyrus activity correlated with the ability to express the target emotion through prosody. We conclude that expressing prosodic emotions (vs. neutral intonation) requires generic brain regions involved in comprehending numerous aspects of language, emotion-related processes such as experiencing emotions, and in the time-critical integration of speech information.

An examination of the language construct in NIMH's research domain criteria: Time for reconceptualization!

The National Institute of Mental Health's Research Domain Criteria (RDoC) Initiative "calls for the development of new ways of classifying psychopathology based on dimensions of observable behavior." As a result of this ambitious initiative, language has been identified as an independent construct in the RDoC matrix. In this article, we frame language within an evolutionary and neuropsychological context and discuss some of the limitations to the current measurements of language. Findings from genomics and the neuroimaging of performance during language tasks are discussed in relation to serious mental illness and within the context of caveats regarding measuring language. Indeed, the data collection and analysis methods employed to assay language have been both aided and constrained by the available technologies, methodologies, and conceptual definitions. Consequently, different fields of language research show inconsistent definitions of language that have become increasingly broad over time. Individually, they have also shown significant improvements in conceptual resolution, as well as in experimental and analytic techniques. More recently, language research has embraced collaborations across disciplines, notably neuroscience, cognitive science, and computational linguistics and has ultimately re-defined classical ideas of language. As we move forward, the new models of language with their remarkably multifaceted constructs force a re-examination of the NIMH RDoC conceptualization of language and thus the neuroscience and genetics underlying this concept. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Vocal acoustic analysis as a biometric indicator of information processing: implications for neurological and psychiatric disorders

Vocal expression reflects an integral component of communication that varies considerably within individuals across contexts and is disrupted in a range of neurological and psychiatric disorders. There is reason to suspect that variability in vocal expression reflects, in part, the availability of "on-line" resources (e.g., working memory, attention). Thus, understanding vocal expression is a potentially important biometric index of information processing, not only across but within individuals over time. A first step in this line of research involves establishing a link between vocal expression and information processing systems in healthy adults. The present study employed a dual attention experimental task where participants provided natural speech while simultaneously engaged in a baseline, medium or high nonverbal processing-load task. Objective, automated, and computerized analysis was employed to measure vocal expression in 226 adults. Increased processing load resulted in longer pauses, fewer utterances, greater silence overall and less variability in frequency and intensity levels. These results provide compelling evidence of a link between information processing resources and vocal expression, and provide important information for the development of an automated, inexpensive and uninvasive biometric measure of information processing.

Speech deficits in serious mental illness: a cognitive resource issue?

Speech deficits, notably those involved in psychomotor retardation, blunted affect, alogia and poverty of content of speech, are pronounced in a wide range of serious mental illnesses (e.g., schizophrenia, unipolar depression, bipolar disorders). The present project evaluated the degree to which these deficits manifest as a function of cognitive resource limitations. We examined natural speech from 52 patients meeting criteria for serious mental illnesses (i.e., severe functional deficits with a concomitant diagnosis of schizophrenia, unipolar and/or bipolar affective disorders) and 30 non-psychiatric controls using a range of objective, computer-based measures tapping speech production ("alogia"), variability ("blunted vocal affect") and content ("poverty of content of speech"). Subjects produced natural speech during a baseline condition and while engaging in an experimentally-manipulated cognitively-effortful task. For correlational analysis, cognitive ability was measured using a standardized battery. Generally speaking, speech deficits did not differ as a function of SMI diagnosis. However, every speech production and content measure was significantly abnormal in SMI versus control groups. Speech variability measures generally did not differ between groups. For both patients and controls as a group, speech during the cognitively-effortful task was sparser and less rich in content. Relative to controls, patients were abnormal under cognitive load with respect only to average pause length. Correlations between the speech variables and cognitive ability were only significant for this same variable: average pause length. Results suggest that certain speech deficits, notably involving pause length, may manifest as a function of cognitive resource limitations. Implications for treatment, research and assessment are discussed.

What do we really know about blunted vocal affect and alogia? A meta-analysis of objective assessments

Deficits in nonverbal vocal expression (e.g., blunted vocal affect, alogia) are a hallmark of schizophrenia and are a focus of the Research Domain Criteria initiative from the National Institute of Mental Health. Results from studies using symptom rating scales suggest that these deficits are profound; on the order of four to six standard deviations. To complement this endeavor, we conducted a meta-analysis of studies employing objective analysis of natural speech in patients with schizophrenia and nonpsychiatric controls. Thirteen studies, collectively including 480 patients with schizophrenia and 326 nonpsychiatric controls, were identified. There was considerable variability across studies in which aspects of vocal communication were examined and in the magnitudes of deficit. Overall, speech production (reflecting alogia) was impaired at a large effects size level (d=-.80; k=13), whereas speech variability (reflecting blunted affect) was much more modest (d=-.36; k=2). Regarding the former, this was largely driven by measures of pause behavior, as opposed to other aspects of speech (e.g., number of words/utterances). On the other hand, ratings of negative symptoms across these studies suggested profound group differences (d=3.54; k=4). These data suggest that only certain aspects of vocal expression are affected in schizophrenia, and highlight major discrepancies between symptom rating and objective-based measures. The discussion centers on advancing objective analysis for understanding vocal expression in schizophrenia and for identifying and defining more homogenous patient subsets for study.

Computerized facial analysis for understanding constricted/blunted affect: initial feasibility, reliability, and validity data

Diminished expression is a diagnostic feature of a range of schizophrenia-spectrum disorders/conditions and is often unresponsive to treatment, is present across premorbid, first episode and various clinical states, and is considered a poor prognostic indicator. Surprisingly, little is known about diminished expression. The present study sought to address this issue by evaluating a commercially-available computerized facial analysis software for understanding diminished expressivity. We analyzed natural facial expression from a series of laboratory interaction tasks in 28 individuals with psychometric schizotypy - defined as the personality organization reflecting a putative genetic schizophrenia liability, and 26 matched controls. We evaluated (a) feasibility - defined in terms of the number of video frames recognized by the software, (b) reliability - defined in terms of correlations between facial expression variables across the three laboratory interactions, and (c) construct validity - defined in terms of relationships to clinical variables. For most subjects (~80%), approximately three-quarters of the video frames were analyzable by the software; however, a minority of the videos were essentially unreadable. The facial expression variables showed excellent reliability across interaction conditions. In terms of construct validity, facial expression variables were significantly related to a measure of psychoticism, tapping subjective cognitive concerns and "first-rank" schizophrenia symptoms, but were generally not different between groups. Facial expression variables were generally not significantly related to measures of depression, anxiety, paranoia or, surprisingly, self-reported negative schizotypy. While computerized facial analysis appears to be a reliable and promising method of understanding diminished expressivity across the schizophrenia-spectrum, some work remains. Implications are discussed.

Psychiatric symptom versus neurocognitive correlates of diminished expressivity in schizophrenia and mood disorders

Diminished expressivity is a poorly understood, but important construct for a range of mental diseases. In the present study, we employed computerized acoustic analysis of natural speech to understand diminished expressivity in patients with schizophrenia and mood disorders. We were interested in the degree to which speech characteristics tapping alogia (i.e., average pause duration) and blunted affect (i.e., prosody computed from fundamental frequency and intensity) reflected psychiatric symptoms (i.e., depression, anxiety, paranoia and bizarre behavior) versus neurocognitive deficits. Twenty-six subjects with schizophrenia and 22 subjects with mood disorders provided speech samples in response to a variety of laboratory stimuli and completed neuropsychological batteries assessing a range of abilities. For both the schizophrenia and mood disorder groups, attentional coding deficits were significantly correlated with increased pause time (at large effect size levels) and, for the schizophrenia group only, reduced prosody (also at a large effect size level). For the mood disorder but not the schizophrenia group, increased average pause time was also significantly associated with neurocognitive deficits on a range of other tests (medium to large effect size levels). Psychiatric symptoms were not significantly associated with speech characteristics for either group (generally, negligible effect sizes). These results suggest that there is a link between expressivity and neurocognitive dysfunctions for both patients with schizophrenia and mood disorders. Implications and future research directions are discussed.

On the boundaries of blunt affect/alogia across severe mental illness: implications for Research Domain Criteria

There is growing awareness that reduced expressive behaviors (e.g., blunt affect, alogia, psychomotor retardation) are characteristic of a range of psychiatric conditions, including mood and schizophrenia-spectrum disorders. From a Research Domain Criteria (RDoC) perspective, it would be critical to determine whether these symptoms manifest similarly across diagnostic groups--as they may share common pathophysiological underpinnings. The present study employed computerized acoustic analysis of speech produced in reaction to a range of visual stimuli in 48 stable outpatients with schizophrenia and mood disorders to offer preliminary understanding of this issue. Speaking assessments were administered 1 week-apart to examine how temporal stability might vary as a function of clinical diagnosis and symptom severity. Speech characteristics generally did not differ between groups and were similarly, and for the most part, highly stable over time. Aspects of speech were significantly associated with severity of psychosis and negative symptoms, but not with clinical depression/anxiety severity. Moreover, stability of speech characteristics generally did not vary as a function of diagnostic group or clinical severity. The magnitudes of group differences were almost exclusively in the negligible to small range. Speech production was associated with social functioning deficits. In sum, these preliminary data suggest that speech variables tap a stable and clinically important facet of psychopathology that cut across diagnostic categories. Computerized acoustic analysis of speech appears to be a promising method for understanding the pathological manifestation of these variables.

A laboratory study of affectivity in schizotypy: subjective and lexical analysis

Affective dysfunction is a defining schizotypy feature; yet the majority of studies examining affective dysfunction have largely relied on self-report of trait affect, which reflects only one dimension of emotional experience. Emerging research has explored a second dimension, state affect, using laboratory manipulation, with most finding that schizotypal participants report experiencing less positive/more negative affect than controls. This study expands upon this topic by examining patterns of state affect in psychometrically identified schizotypy through self-report and lexical expression in reaction to emotionally valenced photos. Overall, the schizotypy group reported less positive/more negative affect across affect induction conditions. Both schizotypy and control groups' affect ratings were similar following the unpleasant stimuli; but the schizotypy group's ratings remained significantly less positive/more negative than the control group following the pleasant stimuli. This pattern suggests that the schizotypy group experienced a deficit in emotional reactivity compared to controls in pleasant situations. The schizotypy group also used a higher percentage of negative words and a lower percentage of positive words in vocalized reactions during the pleasant, but not unpleasant, affect induction condition. These results reveal a specific pattern of "in-the-moment" affective dysfunction unique to pleasant situations that is consistent across both subjective experience and lexical expression.