NEP Core: Natural Emotion Processor

Module of the GPR-Framework (Generative & Programmable Response Framework)

Overview

NEP is a high-precision diagnostic engine designed to quantify Affective Deviation (\Delta A) by decoupling raw input signals into Logical Content (S') and Emotional Schemas. It utilizes a Dual-Translation Emotive Bridge to map user states onto a 4D JAST-V bipolar space, providing the necessary data for XLM-RoBERTa semantic analysis and HITL-Reinforcement Learning loops.

Core Architecture

• Differential Engine: Computes the Z-score/scalar variance between the Current Abstract (real-time biometric data) and the Base Abstract (user's neutral/habitual profile).
• E-Token Generator: Packages the logical schema and the emotional deviation vector into a dense token for Transformer-based policy decisions.
• Multimodal Analyzers:
• TextAnalyser: 14 stylometric factors (lexical diversity, punctuation frequency, contraction rates).
• AudioAnalyser: Acoustic signal decomposition using Parselmouth/Librosa (Pitch/F0, Jitter, Shimmer, Spectral Centroids).
• VideoAnalyser: (Placeholder) Future integration for micro-expression and facial action unit (FAU) tracking.

File Structure

• main.py: Entry point; contains the DifferentialEngine and EToken packaging logic.
• TextAnalyser.py: Psycholinguistic diagnostic tool.
• AudioAnalyser.py: DSP engine for glottal source and vocal tract filter modeling.
• WhiteList.dict: FOSS/Technical terminology whitelist to prevent false-positive "typo" or "slang" detection in technical discourse.

E-Token Specification

The EToken namedtuple consists of:

1. logical_schema: The raw semantic data (S').
2. emotion_schema: S' prefixed with the quantified \Delta A vector.
3. base_abstract: The reference neutral profile.
4. current_abstract: The momentary feature set.

Usage

from NEP import tokeniser

# Example: Process text and audio input
e_token = tokeniser(
    text="The Arch Linux kernel is performing within parameters, brother.",
    audio_path="input_signal.wav",
    base_profiles=my_stored_baseline
)

# Output for XLM-R / GPRF Decision Layer
print(e_token.emotion_schema)