Voice Activity Detection (VAD) and Personal Voice Activity Detection (PVAD) are fundamental components in modern voice-based human–machine interaction systems. While VAD distinguishes speech from non-speech segments, PVAD further identifies whether the detected speech belongs to a specific target speaker, enabling more robust performance in multi-speaker environments. Recently, the Flexible Dynamic Encoder RNN (FDE-RNN) has demonstrated state-of-the-art performance on PVAD tasks by leveraging a detachable Personalization module (P-module) built upon a Dynamic Encoder RNN backbone. However, the Long Short-Term Memory (LSTM) networks employed throughout FDE-RNN inherently suffer from sequential processing constraints that prevent parallelization across time steps, and their fixed-size hidden state may restrict representational capacity for fine-grained speaker discrimination. In this paper, we propose FDE-Mamba, which replaces all three LSTM components in FDE-RNN—the Prediction RNN, the Encoder RNN, and the P-module temporal model—with independent Mamba blocks, each equipped with a selective state space mechanism and an expansion layer for enriched feature representation. The proposed architecture retains the weighted residual connection, FiLM-based speaker embedding fusion, and parallel training strategy of the original FDE-RNN without modification. Experimental results on the LibriSpeech corpus demonstrate that FDE-Mamba achieves a PVAD mAP of 0.9605, representing a 1.97% improvement over the reproduced FDE-RNN baseline (0.9419), along with an accuracy improvement from 86.85% to 89.87% and a 3.16× reduction in real-time factor owing to the memory-efficient linear recurrences of the Mamba selective scan during inference, alongside its inherent parallelizability during training. Ablation studies further confirm that both the D skip connection and the expansion layer within each Mamba block contribute meaningfully to the observed performance gains, validating the effectiveness of each architectural design choice. These results suggest that Mamba is a compelling alternative to LSTM for temporal modeling in PVAD systems, and that the proposed integration provides a design blueprint for future selective SSM applications in gated PVAD architectures.

This study presents HGRN2-based Flexible Dynamic Encoder Personal VAD (FDE-HGRN2), a recurrent framework for personal voice activity detection (PVAD). Building on the original LSTM-based FDE-RNN backbone, we replace all recurrent modules with the recently introduced HGRN2 gated linear RNN and adopt a cosine-annealing learning rate schedule to improve both detection accuracy and efficiency. HGRN2 uses gated linear recurrence with non-parametric state expansion, enlarging the recurrent state without increasing the number of trainable parameters and enabling more expressive long-range temporal modeling than conventional LSTMs. We evaluate FDE-HGRN2 on a LibriSpeech-derived PVAD benchmark, where multi-speaker mixtures are constructed by concatenating one to three speakers per utterance and randomly designating a target speaker, following established PVAD data construction practices to ensure direct comparability with prior work. The system uses 40-dimensional Mel-filterbank features as acoustic inputs and conditions the detector on 256-dimensional d-vector embeddings extracted from a pretrained speaker verification network. Experimental results show that FDE-HGRN2 consistently outperforms the original FDE-RNN baseline and several state-of-the-art PVAD models in terms of mean Average Precision and frame-level accuracy, while reducing the parameter count of the recurrent backbone by roughly 15% and yielding substantially smaller models than many competing systems. These findings indicate that HGRN2 provides a more temporally expressive and parameter-efficient alternative to LSTM for PVAD, offering a favorable accuracy–efficiency trade-off for real-world, deployment-oriented personalized speech interfaces.