Recent work has begun exploring neural acoustic word
embeddings---fixed-dimensional vector representations of arbitrary-length
speech segments corresponding to words. Such embeddings are applicable to
speech retrieval and recognition tasks, where reasoning about whole words may
make it possible to avoid ambiguous sub-word representations. The main idea is
to map acoustic sequences to fixed-dimensional vectors such that examples of
the same word are mapped to similar vectors, while different-word examples are
mapped to very different vectors. In this work we take a multi-view approach to
learning acoustic word embeddings, in which we jointly learn to embed acoustic
sequences and their corresponding character sequences. We use deep
bidirectional LSTM embedding models and multi-view contrastive losses. We study
the effect of different loss variants, including fixed-margin and
cost-sensitive losses. Our acoustic word embeddings improve over previous
approaches for the task of word discrimination. We also present results on
other tasks that are enabled by the multi-view approach, including cross-view
word discrimination and word similarity.

本研究用多视角学习、双向 LSTM 嵌入模型和多视角对比损失函数等方法学习神经音频单词嵌入，实现在语音检索和识别任务中避免模棱两可的子词表示，改善词汇区分效果，并在交叉视图词区分和词相似度等其他任务中展示了应用前景。