Two-Way Translation: Real-World Scenarios

Testing where Lucy's translation could break in practice

Critical Failure Modes

1. Transcription Accuracy Under 90%

When guests use accents, code-switch, or speak quickly, Deepgram accuracy drops to 67-85%. This cascades all downstream logic.

2. Tone/Emotion Loss Across Languages

Sarcasm, irony, and cultural humor don't translate literally. The LLM processes semantic meaning but misses emotional intent.

3. Latency Kills Naturalness

Multi-intent utterances → long TTS responses → 2-3 second waits break conversational flow. Humans expect <500ms.

4. Language Detection Confusion

Code-switching, accents, and bilingual guests confuse language detection. System defaults to English, losing context.

5. Cultural Reference Blindness

TV shows, idioms, slang, and cultural markers don't survive translation. System responds literally to poetic/witty input.