SpikCommander: A High-performance Spiking Transformer with Multi-view Learning for Efficient Speech Command Recognition
Abstract
Spiking neural networks (SNNs) offer a promising path toward energy-efficient speech command recognition (SCR) by leveraging their event-driven processing paradigm. However, existing SNN-based SCR methods often struggle to capture rich temporal dependencies and contextual information from speech due to limited temporal modeling and binary spike-based representations. To address these challenges, we first introduce the multi-view spiking temporal-aware self-attention (MSTASA) module, which combines effective spiking temporal-aware attention with a multi-view learning framework to model complementary temporal dependencies in speech commands. Building on MSTASA, we further propose SpikCommander, a fully spike-driven transformer architecture that integrates MSTASA with a spiking contextual refinement channel MLP (SCR-MLP) to jointly enhance temporal context modeling and channel-wise feature integration. We evaluate our method on three benchmark datasets: the Spiking Heidelberg Dataset (SHD), the Spiking Speech Commands (SSC), and the Google Speech Commands V2 (GSC). Extensive experiments demonstrate that SpikCommander consistently outperforms state-of-the-art (SOTA) SNN approaches with fewer parameters under comparable time steps, highlighting its effectiveness and efficiency for robust speech command recognition.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of SpikCommander, a high-performance spiking transformer architecture that effectively models temporal dependencies in speech command recognition tasks. This work significantly advances the field of neuromorphic computing by addressing key challenges in SNNs and demonstrating superior performance compared to existing methods.
Methodology
The paper introduces a novel architecture, SpikCommander, which integrates a multi-view spiking temporal-aware self-attention (MSTASA) module and a spiking contextual refinement MLP (SCR-MLP). This approach is innovative in addressing the limitations of existing spiking neural networks (SNNs) for speech command recognition by effectively modeling temporal dependencies through a multi-view learning framework. The architecture's design is well-justified and includes a detailed explanation of the spiking neuron model and the attention mechanisms employed, showcasing a comprehensive understanding of the challenges in SNNs.
Results & evaluation
The authors conduct extensive experiments on three benchmark datasets (SHD, SSC, and GSC), demonstrating that SpikCommander outperforms state-of-the-art SNN approaches with fewer parameters and comparable time steps. The results are presented clearly, with thorough comparisons against existing methods, and include ablation studies that validate the effectiveness of the proposed components. The experimental design appears robust, although further exploration of additional datasets could enhance generalizability.
Reproducibility
The paper provides a GitHub repository link for code availability, which is crucial for reproducibility. However, the implementation details could be more explicit, particularly regarding hyperparameter settings and training procedures, to facilitate easier reproduction of results by other researchers.
Limitations
The paper does not address potential limitations in the generalizability of the proposed model across different types of speech commands or in real-world applications. Additionally, while the energy efficiency of SNNs is highlighted, practical deployment on neuromorphic hardware remains to be validated.
Broader impact
The research has significant implications for energy-efficient speech command recognition, particularly in resource-constrained environments. The advancements in SNNs could lead to more sustainable AI applications, especially in mobile and embedded systems. The main contribution of this paper is the introduction of SpikCommander, a high-performance spiking transformer architecture that effectively models temporal dependencies in speech command recognition tasks. This work significantly advances the field of neuromorphic computing by addressing key challenges in SNNs and demonstrating superior performance compared to existing methods.
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