Multimodal Sentiment Engine with Transformers
An enterprise orchestration layer ingesting raw A/V meeting streams to calculate aggregate user sentiment by fusing vocal tone, facial mapping, and textual context simultaneously.
Core Technology Stack
Architectural Constraints
Human conversational irony destroys strict textual LLM accuracy (e.g., saying "this is great" in a purely sarcastic tone registers textually as mathematically positive).
System Implementation
Fusing multimodal inference vectors. The model cross-references the textual matrix with localized frequency pitch distortions and localized facial micro-expressions prior to establishing final dimensional categorization.
Infrastructure Deep Dive
Utilizes FFmpeg to forcefully sever binary AV wrappers into discrete frames and localized WAV streams. Data is fired parallelized into distinct Whisper Audio models and localized vision processors, executing continuous cross-attention matrix scoring.