Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches
Table 2: Average precision on the dev set, using classifier-based embeddings. S = # stacked layers, F = # fully connected layers.
['[ITALIC] S', '[ITALIC] F', 'GRU AP', 'LSTM AP']
The best performance in this experiment is achieved by the LSTM network with S=F=3. However, performance still seems to be improving with additional layers, suggesting that we may be able to further improve performance by adding even more layers of either type. However, we fixed the model to S= F=3 in order to allow for more experimentation and analysis within a reasonable time. When only one fully connected layer is used, the GRU networks outperform the LSTMs given a sufficient number of stacked layers. On the other hand, once we add more fully connected layers, the LSTMs outperform the GRUs.