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      A survey of state-of-the-art approaches for emotion recognition in text

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          A circumplex model of affect.

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            Glove: Global Vectors for Word Representation

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              Framewise phoneme classification with bidirectional LSTM and other neural network architectures.

              In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent Neural Nets (RNNs) and time-windowed Multilayer Perceptrons (MLPs). Our results support the view that contextual information is crucial to speech processing, and suggest that BLSTM is an effective architecture with which to exploit it.
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                Author and article information

                Journal
                Knowledge and Information Systems
                Knowl Inf Syst
                Springer Science and Business Media LLC
                0219-1377
                0219-3116
                August 2020
                March 18 2020
                August 2020
                : 62
                : 8
                : 2937-2987
                Article
                10.1007/s10115-020-01449-0
                f5f5ea4e-ce43-4604-a366-f9b5baeb463f
                © 2020

                http://www.springer.com/tdm

                http://www.springer.com/tdm

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