Most speech recognition systems that use deep learning aim to simplify the processing pipeline
by training a single model to directly map speech signals to their corresponding text transcriptions.
Unlike traditional ASR systems that require multiple components to extract and model features,
such as HMMs and GMMs, end-to-end models do not rely on hand-designed components [19,
305]. Instead, end-to-end ASR systems use DNNs to learn acoustic and linguistic representations
directly from the input speech signals [305]. One popular type of end-to-end model is the encoder-
decoder model with attention. This model uses an encoder network to map input audio signals to
hidden representations, and a decoder network to generate text transcriptions from the hidden
representations. During the decoding process, the attention mechanism enables the decoder to
selectively focus on different parts of the input signal [305].
AReviewofDeepLearningTechniquesforSpeechProcessing