A speech synthesizer produces a detailed waveform, but a listener need not hear every detail. Strong sounds can mask weaker neighbors, creating context-dependent "blind spots" in hearing. Could neural text-to-speech (TTS) spend less computation on those details? We investigate a first step: how the placement of spectral degradation affects completed speech from Piper and Kokoro. At matched time-frequency bin-removal budgets, a simple low-energy-first policy achieves higher mean wideband PESQ than random deletion and the tested psychoacoustic masking criteria. At 50% removal in the spreading-matrix comparison, energy, masking, and random policies score 4.46, 3.60, and 1.19 on Piper, and 3.42, 2.10, and 1.22 on Kokoro. Spectrograms and masking maps expose where these perturbations fall and why a model's below-threshold regions cannot automatically be treated as inaudible. Calibration diagnostics show substantial threshold sensitivity, while a small single-listener pilot establishes no transparent removal budget. CPU profiles identify waveform decoding as the main measured kernel cost, but a tested dynamic-INT8 Kokoro decoder runs slower than FP32 on both CPUs. The results establish energy ranking as a strong oracle baseline and motivate testing perceptual allocation inside a decoder. They do not yet demonstrate saved computation: the decisions here use already synthesized audio, and quality metrics are evaluated at 16 kHz. We release scripts, results, audio examples, and a listening-study instrument.

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