Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool.

BACKGROUND: Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. METHODS: Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. RESULTS: The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. CONCLUSIONS: Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

[Recognizing depression through computational language analysis]. / Herkennen van depressie met computationele taalanalyse.

BACKGROUND: Currently, clinical practice lacks a usable biomarker for the detection and differentiation of depression. Such a biomarker may be found in speech, from which important information can be distilled using automated speech analysis. AIM: To provide an overview of the fast-developing field of automated speech analysis for depression. METHOD: We summarize the current literature on speech features in depression. RESULTS: Current computational models can detect depression with high accuracy, rendering them applicable for diagnostic tools based on automatic speech analysis. Such tools are developing at a fast rate. CONCLUSION: Some challenges are still in the way of clinical implementation. For example, results differ largely between studies due to much variation in methodology. Furthermore, privacy and ethical issues need to be addressed before tools can be used.

Negative content in auditory verbal hallucinations: a natural language processing approach.

INTRODUCTION: Negative content of auditory verbal hallucinations (AVH) is a strong predictor of distress and impairment. This paper quantifies emotional voice-content in order to explore both subjective (i.e. perceived) and objectively (i.e. linguistic sentiment) measured negativity and investigates associations with distress. METHODS: Clinical and non-clinical participants with frequent AVH (n = 40) repeated and recorded their AVH verbatim directly upon hearing. The AVH were analyzed for emotional valence using Pattern, a rule-based sentiment analyzer for Dutch. The AVH of the clinical individuals were compared to those of non-clinical voice-hearers on emotional valence and associated with experienced distress. RESULTS: The mean objective valence of AVH in patients was significantly more negative than those of non-clinical voice-hearers. In the clinical individuals a larger proportion of the voice-utterances was negative (34.7% versus 18.4%) in objective valence. The linguistic valence of the AVH showed a significant, strong association with the perceived negativity, amount of distress and disruption of life, but not with the intensity of distress. CONCLUSIONS: Our results indicate that AVH of patients have a more negative linguistic content than those of non-clinical voice-hearers, which is associated with the experienced distress. Thus, patients not only perceive their voices as more negative, objective analyses confirm this.

Assessing coherence through linguistic connectives: Analysis of speech in patients with schizophrenia-spectrum disorders.

BACKGROUND: Incoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD. METHODS: Connectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups. RESULTS: SSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p < .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p < .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy. DISCUSSION: Our results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD.

A data-driven linguistic characterization of hallucinated voices in clinical and non-clinical voice-hearers.

BACKGROUND: Auditory verbal hallucinations (AVHs) are heterogeneous regarding phenomenology and etiology. This has led to the proposal of AVHs subtypes. Distinguishing AVHs subtypes can inform AVHs neurocognitive models and also have implications for clinical practice. A scarcely studied source of heterogeneity relates to the AVHs linguistic characteristics. Therefore, in this study we investigate whether linguistic features distinguish AVHs subtypes, and whether linguistic AVH-subtypes are associated with phenomenology and voice-hearers' clinical status. METHODS: Twenty-one clinical and nineteen non-clinical voice-hearers participated in this study. Participants were instructed to repeat verbatim their AVHs just after experiencing them. AVH-repetitions were audio-recorded and transcribed. AVHs phenomenology was assessed using the Auditory Hallucinations Rating Scale of the Psychotic Symptom Rating Scales. Hierarchical clustering analyses without a priori group dichotomization were performed using quantitative measures of sixteen linguistic features to distinguish sets of AVHs. RESULTS: A two-AVHs-cluster solution best partitioned the data. AVHs-clusters significantly differed in linguistic features (p < .001); AVHs phenomenology (p < .001); and distribution of clinical voice-hearers (p < .001). The "expanded-AVHs" cluster was characterized by more determiners, more prepositions, longer utterances (all p < .01), and mainly contained non-clinical voice-hearers. The "compact-AVHs" cluster had fewer determiners and prepositions, shorter utterances (all p < .01), more negative content, higher degree of negativity (both p < .05), and predominantly came from clinical voice-hearers. DISCUSSION: Two voice-speech clusters were recognized, differing in syntactic-grammatical complexity and negative phenomenology. Our results suggest clinical voice-hearers often hear negative, "compact-voices", understandable under Broca's right hemisphere homologue and memory-based mechanisms. Conversely, non-clinical voice-hearers experience "expanded-voices", better accounted by inner speech AVHs models.

Quantified language connectedness in schizophrenia-spectrum disorders.

Language abnormalities are a core symptom of schizophrenia-spectrum disorders and could serve as a potential diagnostic marker. Natural language processing enables quantification of language connectedness, which may be lower in schizophrenia-spectrum disorders. Here, we investigated connectedness of spontaneous speech in schizophrenia-spectrum patients and controls and determine its accuracy in classification. Using a semi-structured interview, speech of 50 patients with a schizophrenia-spectrum disorder and 50 controls was recorded. Language connectedness in a semantic word2vec model was calculated using consecutive word similarity in moving windows of increasing sizes (2-20 words). Mean, minimal and variance of similarity were calculated per window size and used in a random forest classifier to distinguish patients and healthy controls. Classification based on connectedness reached 85% cross-validated accuracy, with 84% specificity and 86% sensitivity. Features that best discriminated patients from controls were variance of similarity at window sizes between 5 and 10. We show impaired connectedness in spontaneous speech of patients with schizophrenia-spectrum disorders even in patients with low ratings of positive symptoms. Effects were most prominent at the level of sentence connectedness. The high sensitivity, specificity and tolerability of this method show that language analysis is an accurate and feasible digital assistant in diagnosing schizophrenia-spectrum disorders.

Implementation of automatic speech analysis for early detection of psychiatric symptoms: What do patients want?

Psychiatry is in dire need of a method to aid early detection of symptoms. Recent developments in automatic speech analysis prove promising in this regard, and open avenues for implementation of speech-based applications to detect psychiatric symptoms. The current survey was conducted to assess positions with regard to speech recordings among a group (n = 675) of individuals who experience psychiatric symptoms. Overall, respondents are open to the idea of speech recordings in light of their mental welfare. Importantly, concerns with regard to privacy were raised. Given that speech recordings are privacy sensitive, this requires special attention upon implementation of automatic speech analysis techniques. Furthermore, respondents indicated a preference for speech recordings in the presence of a clinician, as opposed to a recording made at home without the clinician present. In developing a speech marker for psychiatry, close collaboration with the intended users is essential to arrive at a truly valid and implementable method.

Language disturbances in schizophrenia: the relation with antipsychotic medication.

Language disturbances are key aberrations in schizophrenia. Little is known about the influence of antipsychotic medication on these symptoms. Using computational language methods, this study evaluated the impact of high versus low dopamine D2 receptor (D2R) occupancy antipsychotics on language disturbances in 41 patients with schizophrenia, relative to 40 healthy controls. Patients with high versus low D2R occupancy antipsychotics differed by total number of words and type-token ratio, suggesting medication effects. Both patient groups differed from the healthy controls on percentage of time speaking and clauses per utterance, suggesting illness effects. Overall, more severe negative language disturbances (i.e. slower articulation rate, increased pausing, and shorter utterances) were seen in the patients that used high D2R occupancy antipsychotics, while less prominent disturbances were seen in low D2R occupancy patients. Language analyses successfully predicted drug type (sensitivity = 80.0%, specificity = 76.5%). Several language disturbances were more related to drug type and dose, than to other psychotic symptoms, suggesting that language disturbances may be aggravated by high D2R antipsychotics. This negative impact of high D2R occupancy drugs may have clinical implications, as impaired language production predicts functional outcome and degrades the quality of life.