Senior interview questionAsked at Roblox · interview write-up
For an offline audio detection system, outline the ML inference and labeling pipeline: feature extraction, keyword spotting, thresholding, calibration and human labeling.
Model answer
Decode and resample the audio, then compute log-mel spectrograms over short overlapping windows. A keyword-spotting model, a small CNN or a pretrained audio encoder, scores each window; smooth over time and merge adjacent hits into events.
Calibrate scores on held-out data, for example with temperature scaling, then set per-keyword thresholds from precision targets. Send the uncertain band, plus a random sample of confident hits, to human labellers with clear guidelines and agreement checks. Their labels retrain the model and reset the thresholds.
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