End-to-end acoustic-to-word speech recognition models have recently gained
popularity because they are easy to train, scale well to large amounts of
training data, and do not require a lexicon. In addition, word models may also
be easier to integrate with downstream tasks such as spoken language
understanding, because inference (search) is much simplified compared to
phoneme, character or any other sort of sub-word units. In this paper, we
describe methods to construct contextual acoustic word embeddings directly from
a supervised sequence-to-sequence acoustic-to-word speech recognition model
using the learned attention distribution. On a suite of 16 standard sentence
evaluation tasks, our embeddings show competitive performance against a
word2vec model trained on the speech transcriptions. In addition, we evaluate
these embeddings on a spoken language understanding task, and observe that our
embeddings match the performance of text-based embeddings in a pipeline of
first performing speech recognition and then constructing word embeddings from
transcriptions.

本文提出一种方法，使用注意力分配从监督序列到序列的语音到单词识别模型中直接构建上下文语音嵌入。在一系列 16 个标准句子评估任务中，我们的嵌入表现与在语音转录中训练的 word2vec 模型相当。此外，我们在口语理解任务上评估这些嵌入并观察到，我们的嵌入匹配基于文本的嵌入在首先进行语音识别，然后从转录中构建单词嵌入的流水线中的性能。