Exploring Attention Mechanism for Acoustic-based Classification of Speech Utterances into System-Directed and Non-System-Directed
Table 2: Equal error rates of the four frame-based models measured on D1 and D2 test sets.
['[EMPTY]', 'D1', 'D2']
A number of observations can be made from these plots. First, using an LSTM significantly improves the model performance compared to global averaging. Moreover, adding attention to the mix yields an additional boost to the performance with and without the LSTM. Furthermore, having larger improvement with attention on D2 dataset which was not seen in training suggests that the proposed attention mechanism improves model generalization as well.