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Although speech recognition has been widely implemented in software neural networks, a hardware implementation based on energy efficient computing architecture is still missing. Herein, W\/MgO\/SiO<jats:sub>2<\/jats:sub>\/Mo memristor arrays with multilevel resistance states are fabricated, where the weights of the artificial synapses in the memristor array can be tuned precisely by voltage pulses. Based on the array, speech recognition in memristive spiking neural networks (SNNs) with improved supervised tempotron algorithm on Texas Instruments digit sequences (TIDIGITS) dataset is conducted, demonstrating software\u2010comparable accuracy for speech recognition in the memristive SNN. 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