  Acoustic word embeddings --- fixed-dimensional vector representations of
variable-length spoken word segments --- have begun to be considered for tasks
such as speech recognition and query-by-example search. Such embeddings can be
learned discriminatively so that they are similar for speech segments
corresponding to the same word, while being dissimilar for segments
corresponding to different words. Recent work has found that acoustic word
embeddings can outperform dynamic time warping on query-by-example search and
related word discrimination tasks. However, the space of embedding models and
training approaches is still relatively unexplored. In this paper we present
new discriminative embedding models based on recurrent neural networks (RNNs).
We consider training losses that have been successful in prior work, in
particular a cross entropy loss for word classification and a contrastive loss
that explicitly aims to separate same-word and different-word pairs in a
"Siamese network" training setting. We find that both classifier-based and
Siamese RNN embeddings improve over previously reported results on a word
discrimination task, with Siamese RNNs outperforming classification models. In
addition, we present analyses of the learned embeddings and the effects of
variables such as dimensionality and network structure.
