We investigate unsupervised models that can map a variable-duration speech
segment to a fixed-dimensional representation. In settings where unlabelled
speech is the only available resource, such acoustic word embeddings can form
the basis for "zero-resource" speech search, discovery and indexing systems.
Most existing unsupervised embedding methods still use some supervision, such
as word or phoneme boundaries. Here we propose the encoder-decoder
correspondence autoencoder (EncDec-CAE), which, instead of true word segments,
uses automatically discovered segments: an unsupervised term discovery system
finds pairs of words of the same unknown type, and the EncDec-CAE is trained to
reconstruct one word given the other as input. We compare it to a standard
encoder-decoder autoencoder (AE), a variational AE with a prior over its latent
embedding, and downsampling. EncDec-CAE outperforms its closest competitor by
24% relative in average precision on two languages in a word discrimination
task.

本文提出了一种基于编码器 - 解码器一一对应自编码器 (EncDec-CAE) 的无监督学习方法，用于从可变长度的语音以及无标签的语音数据中提取出与固定维度表示形式相对应的词嵌入，从而在无资源语音搜索、发现和索引系统中发挥重要作用，并在词辨别任务中相对于最接近的竞争对手提高了 24% 的平均精度。