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