EMOSEX – emotion prevails over sex during implicit judgment of faces

Appraisals can be influenced by cultural beliefs and stereotypes. In line with this, past research has shown that judgments about the emotional expression of a face are influenced by the face’s sex, and vice versa that judgments about the sex of a person somewhat depend on the person’s facial expression. For example, participants associate anger with male faces, and female faces with happiness or sadness. However, the strength and the bidirectionality of these effects remain debated. Moreover, the interplay of a stimulus’ emotion and sex remains mostly unknown in the auditory domain. To investigate these questions, we created a novel stimulus set of 121 avatar faces and 121 human voices (available at https://bit.ly/2JkXrpy) with matched, fine-scale changes along the emotional (happy to angry) and sexual (male to female) dimensions. In a first experiment (N=76), we found clear evidence for the mutual influence of facial emotion and sex cues on ratings, and moreover for larger implicit (task-irrelevant) effects of stimulus’ emotion than of sex. These findings were replicated and extended in two preregistered studies – one laboratory categorisation study using the same face stimuli (N=108; https://osf.io/ve9an), and one online study with vocalisations (N=72; https://osf.io/vhc9g). Overall, results show that the associations of maleness-anger and femaleness-happiness exist across sensory modalities, and suggest that emotions expressed in the face and voice cannot be entirely disregarded, even when attention is mainly focused on determining stimulus’ sex. We discuss the relevance of these findings for cognitive and neural models of face and voice processing.

Hierarchical cortical networks of “voice patches” for processing voices in human brain

Humans have an extraordinary ability to recognize and differentiate voices. It is yet unclear whether voices are uniquely processed in the human brain. To explore the underlying neural mechanisms of voice processing, we recorded electrocorticographic signals from intracranial electrodes in epilepsy patients while they listened to six different categories of voice and nonvoice sounds. Subregions in the temporal lobe exhibited preferences for distinct voice stimuli, which were defined as “voice patches.” Latency analyses suggested a dual hierarchical organization of the voice patches. We also found that voice patches were functionally connected under both task-engaged and resting states. Furthermore, the left motor areas were coactivated and correlated with the temporal voice patches during the sound-listening task. Taken together, this work reveals hierarchical cortical networks in the human brain for processing human voices.

Glitching the Gendered Voice

Since recording technology first severed the human voice from its originating body, the uncanniness of the sourceless voice has coincided with theoreticizations of queerness. The concept of homosexuality and the proliferation of home phonographs emerged at around the same point in history, and both interventions confounded traditional imaginings of domesticity, intimacy, and communion. As playback technology has grown more responsive and complex, so have defections from normative gender and heterosexuality. Electronic voice processing furthers the confusion of source presented by playback, making electronic music an especially rich field of expression for queer and transgender musicians in particular. This chapter traces the technologically mutated voice through the past century of recording, focusing on its relationship to unruly expressions of gender from electronic music’s origins to its present iterations. Though not only queer artists have participated in this evolutionary process, this chapter highlights the ways trans musicians in particular have used glitch and distortion to rupture the habit of listening for normative gender, sounding new ways of moving and being in their wake.

How to Design a Relevant Corpus for Sleepiness Detection Through Voice?

This article presents research on the detection of pathologies affecting speech through automatic analysis. Voice processing has indeed been used for evaluating several diseases such as Parkinson, Alzheimer, or depression. If some studies present results that seem sufficient for clinical applications, this is not the case for the detection of sleepiness. Even two international challenges and the recent advent of deep learning techniques have still not managed to change this situation. This article explores the hypothesis that the observed average performances of automatic processing find their cause in the design of the corpora. To this aim, we first discuss and refine the concept of sleepiness related to the ground-truth labels. Second, we present an in-depth study of four corpora, bringing to light the methodological choices that have been made and the underlying biases they may have induced. Finally, in light of this information, we propose guidelines for the design of new corpora.

Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview

Voice Processing Systems (VPSes), now widely deployed, have become deeply involved in people’s daily lives, helping drive the car, unlock the smartphone, make online purchases, etc. Unfortunately, recent research has shown that those systems based on deep neural networks are vulnerable to adversarial examples, which attract significant attention to VPS security. This review presents a detailed introduction to the background knowledge of adversarial attacks, including the generation of adversarial examples, psychoacoustic models, and evaluation indicators. Then we provide a concise introduction to defense methods against adversarial attacks. Finally, we propose a systematic classification of adversarial attacks and defense methods, with which we hope to provide a better understanding of the classification and structure for beginners in this field.

Auditory cortical micro-networks show differential connectivity during voice and speech processing in humans

The temporal voice areas (TVAs) in bilateral auditory cortex (AC) appear specialized for voice processing. Previous research assumed a uniform functional profile for the TVAs which are broadly spread along the bilateral AC. Alternatively, the TVAs might comprise separate AC nodes controlling differential neural functions for voice and speech decoding, organized as local micro-circuits. To investigate micro-circuits, we modeled the directional connectivity between TVA nodes during voice processing in humans while acquiring brain activity using neuroimaging. Results show several bilateral AC nodes for general voice decoding (speech and non-speech voices) and for speech decoding in particular. Furthermore, non-hierarchical and differential bilateral AC networks manifest distinct excitatory and inhibitory pathways for voice and speech processing. Finally, while voice and speech processing seem to have distinctive but integrated neural circuits in the left AC, the right AC reveals disintegrated neural circuits for both sounds. Altogether, we demonstrate a functional heterogeneity in the TVAs for voice decoding based on local micro-circuits.

Towards voice based prediction and analysis of emotions in ASD children

Voice processing has proven to be an eminent way of recognizing the various emotions of the people. The objective of this research is to identify the presence of Autism Spectrum Disorder (ASD) and to analyze the emotions of autistic children through their voices. The presented automated voice-based system can detect and classify seven basic emotions (anger, disgust, neutral, happiness, calmness, fear and sadness) expressed by children through source parameters associated with their voices. Various prime voice features such as Mel-frequency Cepstral Coefficients (MFCC) and Spectrogram are extracted and utilized to train a Multi-layer Perceptron (MLP) Classifier to identify possible emotions exhibited by the children thereby assessing their behavioral state. This proposed work therefore helps in the examination of emotions in autistic children that can be used to assess the kind of training and care required to enhance their lifestyle.