  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.
