Hardware Architecture of Emotion Recognition from Speech Features using Recurrent Neural Network and Backpropagation Through Time

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Emotion recognition from speech feature is one of the application where the system needs temporal information in order to produce a correct prediction. On the other hand, recurrent neural network has the advantage of retaining temporal information. This paper proposed a hardware architecture design for emotion recognition system using LSTM (Long Short Term Memory) and BPTT (Backpropagation Through Time). For this application, the proposed architecture consists of a two-layer stacked LSTM with 53 cells on the first layer and 8 cells on the second layer. The architecture is implemented and verified using Verilog language and FPGA.