Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches
Table 1: Final test set results in terms of average precision (AP). Dimensionalities marked with * refer to dimensionality per frame for DTW-based approaches. For CNN and LSTM models, results are given as means over several training runs (5 and 10, respectively) along with their standard deviations.
['Model', 'Dim', 'AP']
Based on development set results, our final embedding models are LSTM networks with 3 stacked layers and 3 fully connected layers, with output dimensionality of 1024 in the case of Siamese networks. Both classifier and Siamese LSTM embedding models outperform all prior results on this task of which we are aware.