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=== Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) === RNNs and LSTMs are particularly suited for sequential data like voice, making them ideal for ASR. ASR involves converting spoken language into written text, which requires understanding temporal dependencies in spoken sequences. LSTM's ability to capture long-term dependencies in sequences has made them an essential component of many state-of-the-art ASR systems.<ref>Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In ''2013 IEEE international conference on acoustics, speech and signal processing'' (pp. 6645-6649). Ieee.</ref><ref>Graves, A., & Jaitly, N. (2014, June). Towards end-to-end speech recognition with recurrent neural networks. In ''International conference on machine learning'' (pp. 1764-1772). PMLR.</ref>
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