title: Improved Acoustic Word Embeddings for Zero-Resource Languages Using Multilingual Transfer
abstract: Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. Such embeddings can form the basis for speech search, indexing and discovery systems when conventional speech recognition is not possible. In zero-resource settings where unlabelled speech is the only available resource, we need a method that gives robust embeddings on an arbitrary language. Here we explore multilingual transfer: we train a single supervised embedding model on labelled data from mu
journal: IEEE/ACM Transactions on Audio Speech and Language Processing