The voice of emotional memory: content-filtered speech in panic disorder, social phobia, and major depressive disorder

Dialect and gender perception in relation to the intelligibility of low-pass and high-pass filtered spontaneous speecha)

Most cues to speech intelligibility are within a narrow frequency range, with its upper limit not exceeding 4 kHz. It is still unclear whether speaker-related (indexical) information is available past this limit or how speaker characteristics are distributed at frequencies within and outside the intelligibility range. Using low-pass and high-pass filtering, we examined the perceptual salience of dialect and gender cues in both intelligible and unintelligible speech. Setting the upper frequency limit at 11 kHz, spontaneously produced unique utterances (n = 400) from 40 speakers were high-pass filtered with frequency cutoffs from 0.7 to 5.56 kHz and presented to listeners for dialect and gender identification and intelligibility evaluation. The same material and experimental procedures were used to probe perception of low-pass filtered and unmodified speech with cutoffs from 0.5 to 1.1 kHz. Applying statistical signal detection theory analyses, we found that cues to gender were well preserved at low and high frequencies and did not depend on intelligibility, and the redundancy of gender cues at higher frequencies reduced response bias. Cues to dialect were relatively strong at low and high frequencies; however, most were in intelligible speech, modulated by a differential intelligibility advantage of male and female speakers at low and high frequencies.

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood

BACKGROUND

Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD).

METHODS

N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally.

RESULTS

Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82).

CONCLUSIONS

Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.

Thin-slice judgments in the clinical context

Clinicians make a variety of assessments about their clients, from judging personality traits to making diagnoses, and a variety of methods are available to do so, ranging from observations to structured interviews. A large body of work demonstrates that from a brief glimpse of another's nonverbal behavior, a variety of traits and inner states can be accurately perceived. Additionally, from these "thin slices" of behavior, even future outcomes can be predicted with some accuracy. Certain clinical disorders such as Parkinson's disease and facial paralysis disrupt nonverbal behavior and may impair clinicians' ability to make accurate judgments. In certain contexts, personality disorders, anxiety, depression, and suicide attempts and outcomes can be detected from others' nonverbal behavior. Additionally, thin slices can predict psychological adjustment to divorce, bereavement, sexual abuse, and well-being throughout life. Thus, for certain traits and disorders, judgments from a thin slice could provide a complementary tool for the clinician's toolbox.

Sex matters: Neural correlates of voice gender perception

The basis for different neural activations in response to male and female voices as well as the question, whether men and women perceive male and female voices differently, has not been thoroughly investigated. Therefore, the aim of the present study was to examine the behavioral and neural correlates of gender-related voice perception in healthy male and female volunteers. fMRI data were collected while 39 participants (19 female) were asked to indicate the gender of 240 voice stimuli. These stimuli included recordings of 3-syllable nouns as well as the same recordings pitch-shifted in 2, 4 and 6 semitone steps in the direction of the other gender. Data analysis revealed a) equal voice discrimination sensitivity in men and women but better performance in the categorization of opposite-sex stimuli at least in men, b) increased responses to increasing gender ambiguity in the mid cingulate cortex and bilateral inferior frontal gyri, and c) stronger activation in a fronto-temporal neural network in response to voices of the opposite sex. Our results indicate a gender specific processing for male and female voices on a behavioral and neuronal level. We suggest that our results reflect higher sensitivity probably due to the evolutionary relevance of voice perception in mate selection.

The effect of fear on paralinguistic aspects of speech in patients with panic disorder with agoraphobia

The present study investigated the effect of fear on paralinguistic aspects of speech in patients suffering from panic disorder with agoraphobia (N = 25). An experiment was conducted that comprised two modules: Autobiographical Talking and Script Talking. Each module consisted of two emotional conditions: Fearful and Happy. Speech was recorded digitally and analyzed using PRAAT, a computer program designed to extract paralinguistic measures from digitally recorded spoken sound. In addition to subjective fear, several speech characteristics were measured as a reflection of psychophysiology: rate of speech, mean pitch and pitch variability. Results show that in Autobiographical Talking speech was slower, had a lower pitch, and a lower pitch variability than in Script Talking. Pitch variability was lower in Fearful than in Happy speech. The findings indicate that paralinguistic aspects of speech, especially pitch variability, are promising measures to gain information about fear processing during the recollection of autobiographical memories.