[ RESEARCH / 001 ]
The Architecture
of Aural
Intelligence.
Abstract: Current Large Language Models (LLMs) operate on a text-first paradigm. This approach fails to capture paralinguistic signals—tone, prosody, and cultural context—resulting in catastrophic failure rates for non-standard dialects.
01 // The Model
Triangulated Understanding
AuralNet does not treat audio as a flat waveform. We decompose speech into three parallel data streams, allowing models to resolve ambiguity through triangulation.
Acoustic
The Signal. Raw phonetic data captured at 48kHz. Our dataset specifically targets "noisy" environments (street markets, kitchens, transit) to train robustness against real-world interference.
Intent
The Logic. Semantic mapping that transcends literal translation. We annotate for "speaker intent" rather than just word-for-word accuracy, bridging the gap between dialect idioms and standard language models.
Context
The Scene. Metadata layers describing the social dynamic, hierarchy, and physical environment. Crucial for resolving homophones and culturally dependent phrasing.
02 // The Audit
Adversarial Evaluation
We do not test models on "textbook" speech. We test them on the edge cases that break them.
- Standard Eval: Read Script (Studio)
- Aether Eval: Spontaneous Speech (Field)
- Result: Exposes "Hallucination Gap"
03 // Access
Data Availability
Sovereign Licensing
Full commercial access to the AuralNet registry. Raw audio + Triangulated Metadata for model training.
Request License ->Benchmark Audit
One-time adversarial evaluation of your existing model against our standard "Blind Set."
Start Audit ->Diamond Protocol
The Vault. Air-gapped, never-sold data reserved exclusively for Aether Certification. Prevents training contamination.
No Access