Speech recognition is progressively shifting toward edge devices. In this context, achieving low energy consumption and lightweight models is essential. Spiking Neural Networks (SNNs) are promising candidates due to their potential for energy efficiency and their inherent ability to capture temporal dynamics. However, their sequential nature often leads to long training times, and the spiking format of their inputs and outputs requires dedicated strategies for information encoding and decoding. Moreover, SNNs are still not widely adopted, with relatively few studies investigating their application to speech recognition. The goal of this thesis is to evaluate how spiking networks can impact speech recognition tasks.