OBJECTIVE: Characterization of psychotherapy as the \"talking cure\" de-emphasizes the importance of an active listener on the curative effect of talking. We test whether the working alliance and its benefits emerge from expression of voice, per se, or whether active listening is needed. We examine the role of listening in a social identity model of working alliance.
METHODS: University student participants in a laboratory experiment spoke about stress management to another person (a confederate student) who either did or did not engage in active listening. Participants reported their perceptions of alliance, key social-psychological variables, and well-being.
RESULTS: Active listening led to significantly higher ratings of alliance, procedural justice, social identification, and identity leadership, compared to no active listening. Active listening also led to greater positive affect and satisfaction. Ultimately, an explanatory path model was supported in which active listening predicted working alliance through social identification, identity leadership, and procedural justice.
CONCLUSIONS: Listening quality enhances alliance and well-being in a manner consistent with a social identity model of working alliance, and is a strategy for facilitating alliance in therapy.

OBJECTIVE: To compare and correlate musical performance anxiety (MPA) and vocal self-perception among amateur evangelical singers, focusing on the interaction between anxiety and aspects of performance in this sample.
METHODS: This study employed a cross-sectional and quantitative approach, involving 75 amateur gospel singers from evangelical churches, aged between 18 and 59 years. Data collection included the administration of a sample identification and characterization questionnaire, the Brazilian Portuguese version of the Kenny Music Performance Anxiety Inventory (K-MPAI), and the Singing Voice Handicap Index (S-VHI). The descriptive analysis used absolute and relative frequencies, measures of central tendency, and dispersion (mean and standard deviation [SD]). To compare the vocal self-assessment protocols and performance aspects, the Kruskal-Wallis test was applied. Spearman\'s correlation test was used for correlation analysis. All analyses were conducted with a significance level set at 5% (P < 0.05).
RESULTS: Vocal warm-up and cool-down activities, vocal discomfort after performance, and vocal self-assessment were significantly associated with scores on S-VHI, and the variable \"instruments louder than voices\" was associated with the K-MPAI score. Participants exhibited a mean K-MPAI score of 85.12 points (SD ± 36.6), and the vocal handicap of the sample had a mean score of 45.22 (SD ± 32.3). There was no statistically significant correlation between the protocols.
CONCLUSIONS: Incorporating vocal warm-up and cool-down activities was significantly associated with lower scores on S-VHI. Conversely, those experiencing postperformance vocal discomfort exhibited higher scores on S-VHI. Moreover, the absence of correlation between the assessment protocols suggests that while significant levels of voice handicap were observed, a direct link to MPA cannot be definitively established. Overall, these findings contribute to a nuanced understanding of the multifaceted factors shaping vocal health and performance among amateur evangelical singers, thereby guiding future research and interventions in this field.

The developmental trajectory of emotion recognition (ER) skills is thought to vary by nonverbal modality, with vocal ER becoming mature later than facial ER. To investigate potential neural mechanisms contributing to this dissociation at a behavioural level, the current study examined whether youth\'s neural functional connectivity during vocal and facial ER tasks showed differential developmental change across time. Youth ages 8-19 (n = 41) completed facial and vocal ER tasks while undergoing functional magnetic resonance imaging, at two timepoints (1 year apart; n = 36 for behavioural data, n = 28 for neural data). Partial least squares analyses revealed that functional connectivity during ER is both distinguishable by modality (with different patterns of connectivity for facial vs. vocal ER) and across time-with changes in connectivity being particularly pronounced for vocal ER. ER accuracy was greater for faces than voices, and positively associated with age; although task performance did not change appreciably across a 1-year period, changes in latent functional connectivity patterns across time predicted participants\' ER accuracy at Time 2. Taken together, these results suggest that vocal and facial ER are supported by distinguishable neural correlates that may undergo different developmental trajectories. Our findings are also preliminary evidence that changes in network integration may support the development of ER skills in childhood and adolescence.

Currently, there is no consensus on the characterization of the human voice. The objective of the present study is to describe the myoelectric behavior of the extrinsic musculature of the larynx in 146 people with normal voice (Spanish speakers), aged between 20 and 50 years old. Different vocal tasks were recorded using a surface electromyograph (SEMG). In all vocal tasks, it was observed that women had higher activation (µV) in the suprahyoid and sternocleidomastoid muscles than men, while men had higher activation in the infrahyoid muscles. SEMG is a valid procedure to help define normal vocal characteristics in the studied population, providing reference values during clinical examination. However, it is necessary to adopt a universal system of assessment tasks and standardized measurement techniques to allow for comparisons with future studies.

People often interact with groups (i.e., ensembles) during social interactions. Given that group-level information is important in navigating social environments, we expect perceptual sensitivity to aspects of groups that are relevant for personal threat as well as social belonging. Most ensemble perception research has focused on visual ensembles, with little research looking at auditory or vocal ensembles. Across four studies, we present evidence that (i) perceivers accurately extract the sex composition of a group from voices alone, (ii) judgments of threat increase concomitantly with the number of men, and (iii) listeners\' sense of belonging depends on the number of same-sex others in the group. This work advances our understanding of social cognition, interpersonal communication, and ensemble coding to include auditory information, and reveals people\'s ability to extract relevant social information from brief exposures to vocalizing groups.

UNASSIGNED: To study the effects of playing mother\'s recorded voice to preterm infants in the NICU on their mothers\' mental health as measured by the Depression, Anxiety and Stress Scale -21 (DASS-21) questionnaire.
UNASSIGNED: This was a pilot single center prospective randomized controlled trial done at a level IV NICU. The trial was registered at clinicaltrials.gov (NCT04559620). Inclusion criteria were mothers of preterm infants with gestational ages between 26wks and 30 weeks. DASS-21 questionnaire was administered to all the enrolled mothers in the first week after birth followed by recording of their voice by the music therapists. In the interventional group, recorded maternal voice was played into the infant incubator between 15 and 21 days of life. A second DASS-21 was administered between 21 and 23 days of life. The Wilcoxon rank-sum test was used to compare DASS-21 scores between the two groups and Wilcoxon signed-rank test was used to compare the pre- and post-intervention DASS-21 scores.
UNASSIGNED: Forty eligible mothers were randomized: 20 to the intervention group and 20 to the control group. The baseline maternal and neonatal characteristics were similar between the two groups. There was no significant difference in the DASS-21 scores between the two groups at baseline or after the study intervention. There was no difference in the pre- and post-interventional DASS-21 scores or its individual components in the experimental group. There was a significant decrease in the total DASS-21 score and the anxiety component of DASS-21 between weeks 1 and 4 in the control group.
UNASSIGNED: In this pilot randomized control study, recorded maternal voice played into preterm infant\'s incubator did not have any effect on maternal mental health as measured by the DASS-21 questionnaire. Data obtained in this pilot study are useful in future RCTs (Randomized Controlled Trial) to address this important issue.

When listeners hear a voice, they rapidly form a complex first impression of who the person behind that voice might be. We characterize how these multivariate first impressions from voices emerge over time across different levels of abstraction using electroencephalography and representational similarity analysis. We find that for eight perceived physical (gender, age, and health), trait (attractiveness, dominance, and trustworthiness), and social characteristics (educatedness and professionalism), representations emerge early (~80 ms after stimulus onset), with voice acoustics contributing to those representations between ~100 ms and 400 ms. While impressions of person characteristics are highly correlated, we can find evidence for highly abstracted, independent representations of individual person characteristics. These abstracted representationse merge gradually over time. That is, representations of physical characteristics (age, gender) arise early (from ~120 ms), while representations of some trait and social characteristics emerge later (~360 ms onward). The findings align with recent theoretical models and shed light on the computations underpinning person perception from voices.

Singing is socially important but constrains voice acoustics, potentially masking certain aspects of vocal identity. Little is known about how well listeners extract talker details from sung speech or identify talkers across the sung and spoken modalities. Here, listeners (n = 149) were trained to recognize sung or spoken voices and then tested on their identification of these voices in both modalities. Learning vocal identities was initially easier through speech than song. At test, cross-modality voice recognition was above chance, but weaker than within-modality recognition. We conclude that talker information is accessible in sung speech, despite acoustic constraints in song.

Parkinson\'s Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80-20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson\'s Disease.

BACKGROUND: The digital era has witnessed an escalating dependence on digital platforms for news and information, coupled with the advent of \"deepfake\" technology. Deepfakes, leveraging deep learning models on extensive data sets of voice recordings and images, pose substantial threats to media authenticity, potentially leading to unethical misuse such as impersonation and the dissemination of false information.
OBJECTIVE: To counteract this challenge, this study aims to introduce the concept of innate biological processes to discern between authentic human voices and cloned voices. We propose that the presence or absence of certain perceptual features, such as pauses in speech, can effectively distinguish between cloned and authentic audio.
METHODS: A total of 49 adult participants representing diverse ethnic backgrounds and accents were recruited. Each participant contributed voice samples for the training of up to 3 distinct voice cloning text-to-speech models and 3 control paragraphs. Subsequently, the cloning models generated synthetic versions of the control paragraphs, resulting in a data set consisting of up to 9 cloned audio samples and 3 control samples per participant. We analyzed the speech pauses caused by biological actions such as respiration, swallowing, and cognitive processes. Five audio features corresponding to speech pause profiles were calculated. Differences between authentic and cloned audio for these features were assessed, and 5 classical machine learning algorithms were implemented using these features to create a prediction model. The generalization capability of the optimal model was evaluated through testing on unseen data, incorporating a model-naive generator, a model-naive paragraph, and model-naive participants.
RESULTS: Cloned audio exhibited significantly increased time between pauses (P<.001), decreased variation in speech segment length (P=.003), increased overall proportion of time speaking (P=.04), and decreased rates of micro- and macropauses in speech (both P=.01). Five machine learning models were implemented using these features, with the AdaBoost model demonstrating the highest performance, achieving a 5-fold cross-validation balanced accuracy of 0.81 (SD 0.05). Other models included support vector machine (balanced accuracy 0.79, SD 0.03), random forest (balanced accuracy 0.78, SD 0.04), logistic regression, and decision tree (balanced accuracies 0.76, SD 0.10 and 0.72, SD 0.06). When evaluating the optimal AdaBoost model, it achieved an overall test accuracy of 0.79 when predicting unseen data.
CONCLUSIONS: The incorporation of perceptual, biological features into machine learning models demonstrates promising results in distinguishing between authentic human voices and cloned audio.