THE USE OF RECURRENT NEURAL NETWORKS IN CONTINUOUS SPEECH RECOGNITION

By Tony A. Robinson, M. Hochberg, S. Renals. 1996

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In Speech & Audio

Citations in the corpus

Learning to Execute (2014, background): “Other interesting applications of recurrent neural networks includes speech recognition (Robinson et al., 1996; Graves et al., 2013), machine translation (Cho et al., 2014; Sutskever et al., 2014), handwriting recognition (Pham et al., 2013; Zaremba et al., 2014b), and many more. ar X iv :1 41 0.”
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin (2015, methodology): “Recurrent neural networks and networks with convolution were also used in speech recognition around the same time [51, 67].”
Recurrent Neural Network Regularization (2014, methodology): “RNNs have also been successfully used for speech recognition (Robinson et al., 1996; Graves et al., 2013) and have recently been applied to machine translation, where they are used for language modeling, re-ranking, or phrase modeling (Devlin et al., 2014; Kalchbrenner & Blunsom, 2013; Cho et al.,…”
End-to-end attention-based large vocabulary speech recognition (2015, background): “This subsection introduces ARSGs and explains the motivation behind our choice of an ARSG for this study.”
Connectionist speech recognition of Broadcast News (2002, methodology): “Further details of the MLPs and RNNs that we have used, including size, format and training issues can be found in Bourlard and Morgan (1996) and Robinson et al. (1996).”
On supervised learning from sequential data with applications for speech regognition (1999, methodology): “For distributions found by likelihood conversion to scaled likelihoods usually only the Viterbi algorithm is used for training (Robinson et al., 1996), giving a distinct best state sequence for every training sequence, although training based on a soft alignment is also possible (Senior and Robinson”
When Are Tree Structures Necessary for Deep Learning of Representations? (2015, background): “like speech recognition (Robinson et al., 1996; Lippmann, 1989; Graves et al., 2013) or handwriting recognition (Graves and Schmidhuber, 2009; Graves, 2012).”
Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, background): “This template is naturally suited for many NLP tasks such as language modeling [2], [63], [64], machine translation [65]–[67], speech recognition [68]–[71], image captioning [72].”