Affective Voice Analysis & Prosody Research

Acoustic Correlates of Discrete Emotion Recognition in Speech

This research investigates the specific acoustic features (pitch, duration, intensity, formant frequencies) that reliably distinguish discrete emotional states in vocal expression. The findings advance our understanding of the phonetic signatures of emotion and enable development of more accurate computational emotion recognition systems.

Neural Substrates of Prosodic Processing During Affective Communication

This study examines brain regions and neural networks activated when listeners process emotional prosody and paralinguistic cues in speech using neuroimaging. The research elucidates the neurobiological mechanisms underlying affective voice perception and emotional inference from tonal patterns.

Cross-Cultural Invariance and Variation in Emotional Prosody Perception

This research examines whether emotional prosody recognition shows universal patterns across linguistically and culturally diverse populations or exhibits significant cultural and linguistic variation. The results illuminate the degree to which emotional voice expression operates as a universal human communication code versus a culturally constructed system.

Voice Quality Modulations as Biomarkers for Affective State and Psychopathology

This investigation analyzes how vocal tract configurations, breathiness, creakiness, and resonance characteristics change across affective states and psychiatric conditions including depression and anxiety. The work establishes voice quality as a sensitive, objective biomarker for monitoring emotional dysregulation and treatment response.

Temporal Dynamics of Prosodic Affect Expression Across Utterance Trajectories

This research tracks how emotional prosody features evolve and change throughout the temporal course of spoken utterances, examining rise times, plateaus, and decay patterns. The findings reveal how affective states unfold dynamically in speech and challenge static models of emotion representation in voice.

Machine Learning Classification of Dimensional Affect From Continuous Voice Signals

This study develops deep learning and supervised learning models that map continuous acoustic features to dimensional representations of affect (valence, arousal, dominance) rather than discrete categories. The research advances computational affective science by capturing the nuanced, graded nature of emotional expression in naturalistic speech.

Acoustic Signatures of Authentic Versus Deceptive Affective Prosody Production

This investigation compares the acoustic features of genuine emotional prosody with acted or simulated emotion to identify reliable markers of authentic affective expression. The research contributes to deception detection and understanding of emotional authenticity in vocal communication.

Vocal Tract Aerodynamics and Laryngeal Physiology During High-Arousal Emotional States

This research employs aerodynamic measures and laryngeal imaging to examine how physiological changes during intense emotion affect voice production mechanisms and acoustic output. The findings illuminate the embodied, physiological foundations of emotional prosody and voice modulation.

Implicit Affective Priming Effects on Real-Time Prosodic Processing and Comprehension

This study investigates whether affective priming stimuli unconsciously influence how listeners process emotional prosody in subsequent speech using eye-tracking and reaction time measures. The research reveals automatic, implicit mechanisms underlying emotional voice perception and affective listening.

Prosodic Entrainment and Affective Synchrony in Naturalistic Dyadic Interactions

This research examines how speaker pairs dynamically synchronize and entrain their prosodic patterns and emotional expression during real-world conversations and therapeutic dialogue. The work advances understanding of social bonding, empathic attunement, and interpersonal emotion regulation through vocal coordination.