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Long short-term memory (LSTM) is normally used in recurrent neural network(RNN) as basic recurrent unit. However,conventional LSTM assumes that the stateat current time step depends on previous time step. This assumption constraintsthe time dependency modeling capability. In this study, we propose a newvariation of LSTM, advanced LSTM (A-LSTM), for better temporal contextmodeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. TheA-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM basedweighted pooling RNN can also complement the state-of-the-art emotionclassification framework. This shows the advantage of A-LSTM.

Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition