DJ Voice Control: Retrieval-Centric Architecture

The DJ Voice Control system adapts the speech-to-order retrieval-centric paradigm for real-time DJ performance control. Instead of matching spoken orders to menu items, we match spoken commands to DJ actions and keyboard shortcuts. This approach provides superior accuracy compared to traditional ASR + NLU pipelines by learning a direct semantic mapping between audio utterances and command intents. **Key Advantages:** - **Sub-second latency**: Direct audio → command matching without transcription - **Robust to variations**: Handles different phrasings, accents, and noise - **No cloud dependency**: Runs entirely locally for zero latency - **Deterministic execution**: Constraint solver ensures valid command combinations - **Continuous improvement**: Easy to add new commands and voice samples **Corpus Statistics:** - ~200 base commands (from your existing command_map) - ~5 variations per command - ~1,000 total documents in retrieval corpus - Flat index sufficient (sub-1ms search) **Key Differences from Café:** - **Noise profile**: Music playback, crowd noise, speaker feedback - **Speaking style**: Louder, more forceful commands - **Latency requirement**: Even tighter (<800ms total) - **Hands-free**: Can't press PTT during performance **Initial Dataset (Your Voice):** 1. **Record command corpus**: - Read each command 3 times (clean) - Record in booth environment (with music) - ~200 commands × 3 reps × 2 conditions = **1,200 samples**