Design and Learning of Output Representations for Speech Recognition

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Deep Learning: Methods and Applications (2014, background): “Basic theories about the internal structure of speech sounds and their relevance to speech recognition technology in terms of the specification, design, and learning of possible output representations of the underlying speech model for speech target sequences are surveyed in [76] and more recently i”
Speech Recognition Using Deep Neural Networks: A Systematic Review (2019, methodology): “Recurrent Neural Networks (RNNs) are considered as a class of deep networks for the use in unsupervised learning in the cases where the depth of the input data sequence can be as large as the length since RNNs allow parameter sharing through the different layers of the network [58], [59].”