Personal voice activity detection (PVAD) identifies whether each detected speech frame originates from a designated target speaker. Modern PVAD systems are typically trained offline and then deployed with frozen model parameters and a fixed, pre-enrolled speaker embedding, leaving them unable to adapt to distribution shifts at inference time such as unseen acoustic environments, changing speaking styles, or mismatches between enrollment and test conditions. Test-time training (TTT) and test-time adaptation have shown promise in language, vision, and several speech tasks, yet their behavior on PVAD has not been studied. In this work, we present an empirical study of two complementary test-time adaptation mechanisms built on top of the recently proposed FDE-Mamba backbone. The first is a VAD-gated TTT adapter, which instantiates the TTT-Linear formulation within the personalization pathway and augments it with a VAD-probability gate and exponential moving-average stabilization, adapting an internal weight matrix on the speaker-conditioned feature stream of each test utterance. The second is TEA (Test-time Embedding Adaptation), a scheme that keeps all model parameters frozen and instead adapts the target speaker d-vector itself via self-supervised objectives at inference time, directly targeting enrollment–test mismatch. We evaluate both mechanisms on the LibriSpeech PVAD benchmark across two backbones (LSTM-based FDE-RNN and Mamba-based FDE-Mamba), reporting category-wise average precision, mean average precision (mAP), accuracy, recall, precision, and real-time factor. We further isolate the effect of a post-hoc Gaussian smoothing step and report that, of all the components we examine, this task-agnostic smoothing accounts for the largest single accuracy gain on the FDE-Mamba backbone (accuracy

89.87 % \to 90.47 %

); the test-time adaptation mechanisms contribute a separate, smaller gain that is concentrated on speaker-discrimination metrics (mAP, precision) rather than on accuracy. Overall, the proposed test-time adaptation yields a consistent but modest improvement over the FDE-Mamba baseline (mAP

0.9605 \to 0.9641

, precision

0.881 \to 0.899

), while slightly reducing recall and increasing inference cost when TEA is enabled. Through ablation studies, we quantify the independent and combined contribution of each component and characterize the recall–precision trade-off introduced by adaptation. These findings, together with a discussion of their limitations and cost–benefit profile, provide a measured baseline and design insights for future work on adaptive PVAD, particularly under stronger acoustic and enrollment mismatches than those captured by the LibriSpeech protocol.

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