Keyword Mamba: Spoken Keyword Spotting with State Space Models
To address the challenge of jointly modeling long-range temporal dependencies and ensuring computational efficiency in keyword spotting (KWS), this paper pioneers the integration of the state-space model Mamba into KWS, proposing a lightweight end-to-end architecture. Departing from the computationally intensive self-attention mechanism of Transformers, our approach leverages Mamba’s selective state-space modeling to efficiently capture long-term temporal dynamics along the sequence axis. The model is trained end-to-end on the Google Speech Commands dataset. Experimental results demonstrate that our method achieves state-of-the-art accuracy (98.2%) while reducing model parameters by 47% and FLOPs by 63% compared to leading CNN-, RNN-, and Transformer-based baselines. This work validates the efficacy and deployment advantages of state-space models for low-latency, resource-constrained KWS applications, establishing a novel paradigm for efficient sequential modeling in speech processing.