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Design a Voice Assistant
Problem Formulation
Clarifying the ML Objective
ML framing: A voice assistant is not one model. It's a cascade of five distinct ML tasks: binary classification (wake-word), sequence-to-sequence generation (ASR), multi-label classification plus sequence tagging (NLU), policy selection (dialogue management), and conditional audio synthesis (TTS).
Most candidates treat this as a single "speech-to-response" problem and immediately start talking about end-to-end models. That's a trap. Each stage has its own objective function, latency budget, and failure mode. Your job in the interview is to decompose the pipeline cleanly before proposing any model.
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