In a 2024 study (Porter et al., ACM Transactions on Computer-Human Interaction ), she demonstrated that users rated an ATM-enabled chatbot as 37% more trustworthy than a baseline model, even when the baseline correctly identified real-time emotions. Her conclusion: consistency across interactions matters more than single-instance accuracy.
Unlike Porter’s longitudinal approach, Manjunath prioritizes sparse temporal sampling —analyzing only 3–5 emotion-relevant frames per second rather than continuous video. In a 2025 field deployment for a teletherapy platform, AffectEdge achieved 89% accuracy in detecting user distress while reducing latency to 120ms (vs. 2.3s for cloud models). valerie porter shailesh manjunath
Critics note that ATM requires significant storage and computational overhead. Porter herself acknowledged that long-term affective traces risk reinforcing negative stereotypes (e.g., persistently treating a user as “angry” after one outburst). This opens the door to Manjunath’s engineering solutions. 3. Shailesh Manjunath: Real-Time Multimodal Affect Processing 3.1 Key Contributions Manjunath’s work, presented at ICML 2023 and IEEE Affective Computing 2025, focuses on lightweight transformer models that fuse facial micro-expressions, vocal prosody, and keystroke dynamics. His signature system, AffectEdge , runs entirely on-device, addressing privacy concerns inherent in cloud-based emotion recognition. In a 2024 study (Porter et al
Manjunath’s critics argue that sparse sampling misses subtle affective shifts over minutes-long conversations. He has responded by developing adaptive sampling rates, but the trade-off between efficiency and emotional granularity remains unresolved. 4. Comparative Analysis and Synthesis | Dimension | Valerie Porter | Shailesh Manjunath | |-----------|----------------|---------------------| | Temporal focus | Long-term affective memory | Real-time, momentary inference | | Primary modality | Conversational history + user modeling | Multimodal (face, voice, text) | | Hardware requirement | Moderate (cloud or hybrid) | Low (edge-only, privacy-preserving) | | Key strength | Trust and relational coherence | Speed, privacy, scalability | | Key weakness | High storage; risk of affective bias | May miss gradual emotional change | In a 2025 field deployment for a teletherapy