Detecting Interrogative Utteranceswith Recurrent Neural Networks
Table 3: Test F1 score of the models trained on CALLHOME. First two columns use neither dropout (shortened as D) nor BN.
['[EMPTY]', 'GRU', 'LSTM', 'GRU, D', 'LSTM, D', 'GRU, BN', 'LSTM, BN']
We can observe that c2 helps the models that only take audio inputs, and batch normalization improves the performance of the models that includes text source as their inputs. The best performance is achieved by a model that takes both input sources (combinational), uses c1 as context function, uses batch normalization for training and uses GRU as the state transition function of the RNN.