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In page Speech recognition:

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Since 2014, much research has considered "end-to-end" ASR. Traditional phonetic-based (i.e., all HMM-based model) approaches required separate components and training for pronunciation, acoustic, and language. End-to-end models learn from all the components at once. This simplifies the training and deployment processes. For example, an n-gram language model is required for all HMM-based systems, and a typical 2025-era n-gram language model often takes gigabytes in memory, making them impractical to deploy on mobile devices.[1] Consequently, ASR systems from Google and Apple (as of 2017) deploy on servers and required a network connection to operate.[citation needed]