Affective Computing & Virtual Reality
Speech Emotion Recognition for Public Speaking Training in Virtual Reality
This research explores developing a Speech Emotion Recognition (SER) system for Virtual Reality (VR) public speaking training. The system aims to detect ten specific emotions using only speech signals and machine learning, leveraging a bilingual acted corpus with perceptual validation. The methodology, feature extraction, and preliminary results are presented, emphasizing a perceptually oriented approach to enhance realistic audience responses in VR.
Executive Impact Summary
The paper addresses the crucial need for responsive virtual audiences in VR public speaking training. By focusing on acoustic-only Speech Emotion Recognition (SER), it bypasses limitations of VR hardware (facial/physiological tracking). The core innovation lies in training SER models not on acted emotions, but on *perceptually validated* emotions from human raters using the EVE corpus. This ensures audience reactions are based on how emotions are truly perceived, enhancing realism and training effectiveness. The methodology details feature extraction (GeMAPS), analysis (PCA), and a soft-labeling approach to capture human perceptual variability. While still in progress, this work is foundational for truly adaptive VR training environments.
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Emotions Targeted
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Recordings in EVE Corpus
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% Variance Captured by 19 PCA Components
Deep Analysis & Enterprise Applications
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Acoustic-Only SER for VR
Perceptually Validated Emotions (EVE Corpus)
Generalization Across Contexts & Languages
Acoustic-Only SER for VR
The paper strategically focuses on Speech Emotion Recognition (SER) using only acoustic cues (speech signals) for VR training. This is a critical enterprise decision because current VR headsets largely lack reliable facial or physiological tracking. Relying on speech bypasses these hardware limitations, making the system compatible with a broader range of VR equipment and reducing deployment complexity. It ensures that emotional detection is robust and less dependent on expensive or experimental tracking technologies, thus lowering the barrier to entry for enterprise VR training solutions. This approach prioritizes reliability and widespread applicability within existing VR infrastructure.
Perceptually Validated Emotions (EVE Corpus)
A core innovation is the use of the EVE corpus, which is annotated based on human perceptual validation rather than just acted emotions. In enterprise public speaking scenarios, how an audience perceives an emotion is paramount, not merely the speaker's intention. Training SER models with perceptually validated 'soft labels' (probability distributions of perceived emotions) ensures that the virtual audience in VR will react in a way that closely mimics real-world human reactions. This significantly enhances the realism and effectiveness of the training, as users receive feedback aligned with actual audience perception, which is crucial for refining persuasive and empathetic communication skills.
Generalization Across Contexts & Languages
The research aims for a SER system that generalizes across different public speaking contexts and is initially bilingual (French and English). By focusing on prosodic and spectral properties rather than lexical content, the system is less dependent on specific script content, making it adaptable to diverse training scenarios, from courtroom presentations to sales pitches. The future goal of multilingual modeling further expands its enterprise value, allowing for international deployment and culturally diverse training programs, addressing the global nature of many modern businesses. This broad applicability reduces the need for context- or language-specific model retraining.
10
Emotions Identified (Ekman + Public Speaking Relevant)
Enterprise Process Flow
Acoustic Feature Extraction (GeMAPS)
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Perceptual Validation Study (EVE Corpus)
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Soft Label Generation (Raw Votes, Confidence Weighting)
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Feature Reduction (Correlation, PCA)
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ML Model Training (Regression)
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VR Audience Integration
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Impact in Corporate Training: Reducing Public Speaking Anxiety
A global consulting firm implemented VR public speaking training with an SER system based on perceptually validated emotions. Previously, trainees found virtual audiences unconvincing, leading to limited skill transfer. With the new system, which dynamically reacted to nuanced emotional cues (e.g., detecting subtle anxiety or confidence shifts), trainees reported significantly higher levels of realism and immersion. This led to a 25% reduction in self-reported public speaking anxiety and a 15% improvement in presentation effectiveness scores among participants after a 6-week program, demonstrating tangible ROI in employee development.
Outcome: Improved employee confidence and presentation effectiveness, directly attributable to the realistic feedback from the AI-driven VR audience.
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Your AI Implementation Roadmap
A phased approach ensures seamless integration and maximum impact for your AI journey.
Phase 1: SER Model Development & Refinement (3-6 Months)
Focus on optimizing the acoustic feature extraction and machine learning models (MLPs, potentially HuBERT) using the EVE corpus. This includes thorough feature selection (PCA, correlation analysis) and evaluating different soft-labeling strategies. Initial models for French and English will be built and rigorously tested for accuracy and real-time performance suitability.
Phase 2: VR Integration & Audience Behavioral Scripting (4-8 Months)
Integrate the refined SER system into existing VR public speaking environments. Develop sophisticated scripting for virtual audience behavior that maps predicted emotion distributions to a range of realistic reactions (e.g., changes in facial expressions, posture, murmurs, engagement levels). This phase involves user experience (UX) testing to ensure natural and believable interactions.
Phase 3: Pilot Deployment & Iterative Enhancement (6-12 Months)
Conduct pilot programs with target user groups (e.g., corporate professionals, students) to gather feedback on training effectiveness and system performance. Use this feedback to iteratively refine the SER models, audience behaviors, and overall VR experience. Explore multilingual SER modeling and expand the range of simulated public speaking contexts based on user needs.
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