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.

本研究基于递归神经网络 (RNN) 提出了新的区分性嵌入模型，探讨了不同的训练损失，通过单词区分任务的评估发现，相比以往的模型，分类器和 Siamese RNNs 都有所改进，其中 Siamese RNNs 的表现更为优异，同时也对嵌入模型和网络结构等变量进行了分析。