Truly Unsupervised Acoustic Word Embeddings Using Weak Top-down Constraints in Encoder-decoder Models

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 in...

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Bibliographic Details

Published in:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 6535 - 6539
Main Author:	Kamper, Herman
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.05.2019
Subjects:	Acoustic word embeddings Acoustics Decoding query-by-example Recurrent neural networks Speech processing Task analysis Training unsupervised learning zero-resource speech processing
ISSN:	2379-190X
Online Access:	Get full text
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