Neurocognitive dynamics of near-threshold voice signal detection and affective voice evaluation

Attention and emotion shape self-voice prioritization in speech processing

Both self-voice and emotional speech are salient signals that are prioritized in perception. Surprisingly, self-voice perception has been investigated to a lesser extent than the self-face. Therefore, it remains to be clarified whether self-voice prioritization is boosted by emotion, and whether self-relevance and emotion interact differently when attention is focused on who is speaking vs. what is being said. Thirty participants listened to 210 prerecorded words spoken in one's own or an unfamiliar voice and differing in emotional valence in two tasks, manipulating the attention focus on either speaker identity or speech emotion. Event-related potentials (ERP) of the electroencephalogram (EEG) informed on the temporal dynamics of self-relevance, emotion, and attention effects. Words spoken in one's own voice elicited a larger N1 and Late Positive Potential (LPP), but smaller N400. Identity and emotion interactively modulated the P2 (self-positivity bias) and LPP (self-negativity bias). Attention to speaker identity modulated more strongly ERP responses within 600 ms post-word onset (N1, P2, N400), whereas attention to speech emotion altered the late component (LPP). However, attention did not modulate the interaction of self-relevance and emotion. These findings suggest that the self-voice is prioritized for neural processing at early sensory stages, and that both emotion and attention shape self-voice prioritization in speech processing. They also confirm involuntary processing of salient signals (self-relevance and emotion) even in situations in which attention is deliberately directed away from those cues. These findings have important implications for a better understanding of symptoms thought to arise from aberrant self-voice monitoring such as auditory verbal hallucinations.

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

A wave-confining metasphere beamforming acoustic sensor for superior human-machine voice interaction

Highly sensitive, source-tracking acoustic sensing is essential for effective and natural human-machine interaction based on voice. It is a known challenge to omnidirectionally track sound sources under a hypersensitive rate with low noise interference using a compact sensor. Here, we present a unibody acoustic metamaterial spherical shell with equidistant defected piezoelectric cavities, referred to as the metasphere beamforming acoustic sensor (MBAS). It demonstrates a wave-confining capability and low self-noise, simultaneously achieving an outstanding intrinsic signal-to-noise ratio (72 dB) and an ultrahigh sensitivity (137 mV_pp/Pa or -26.3 dBV), with a range spanning the daily phonetic frequencies (0 to 1500 Hz) and omnidirectional beamforming for the perception and spatial filtering of sound sources. Moreover, the MBAS-based auditory system is shown for high-performance audio cloning, source localization, and speech recognition in a noisy environment without any signal enhancement, revealing its promising applications in various voice interaction systems.

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.

Neurocognitive processing efficiency for discriminating human non-alarm rather than alarm scream calls

Across many species, scream calls signal the affective significance of events to other agents. Scream calls were often thought to be of generic alarming and fearful nature, to signal potential threats, with instantaneous, involuntary, and accurate recognition by perceivers. However, scream calls are more diverse in their affective signaling nature than being limited to fearfully alarming a threat, and thus the broader sociobiological relevance of various scream types is unclear. Here we used 4 different psychoacoustic, perceptual decision-making, and neuroimaging experiments in humans to demonstrate the existence of at least 6 psychoacoustically distinctive types of scream calls of both alarming and non-alarming nature, rather than there being only screams caused by fear or aggression. Second, based on perceptual and processing sensitivity measures for decision-making during scream recognition, we found that alarm screams (with some exceptions) were overall discriminated the worst, were responded to the slowest, and were associated with a lower perceptual sensitivity for their recognition compared with non-alarm screams. Third, the neural processing of alarm compared with non-alarm screams during an implicit processing task elicited only minimal neural signal and connectivity in perceivers, contrary to the frequent assumption of a threat processing bias of the primate neural system. These findings show that scream calls are more diverse in their signaling and communicative nature in humans than previously assumed, and, in contrast to a commonly observed threat processing bias in perceptual discriminations and neural processes, we found that especially non-alarm screams, and positive screams in particular, seem to have higher efficiency in speeded discriminations and the implicit neural processing of various scream types in humans.