We investigate unsupervised models that can map a variableduration 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 encoderdecoder autoencoder (AE), a variational AE with a prior over its latent embedding, and downsampling. ENCDEC-CAE outperforms its closest competitor by 29% relative in average precision on two languages in a word discrimination task. Index Terms-Acoustic word embeddings, zero-resource speech processing, unsupervised learning, query-by-example.
Kamper REF solved this mismatch by using an unsupervised term discovery system to find sample same-word pairs.
Truly Unsupervised Acoustic Word Embeddings Using Weak Top-down Constraints in Encoder-decoder Models
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science Engineering