Vector quantization and signal compression
Vector quantization and signal compression
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Unsupervised language acquisition
Unsupervised language acquisition
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Unsupervised learning of acoustic sub-word units
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Self-supervised acquisition of vowels in American English
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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In this paper, we develop a new conceptual framework for an important problem in language acquisition, the correspondence problem: the fact that a given utterance has different manifestations in the speech and articulation of different speakers and that the correspondence of these manifestations is difficult to learn. We put forward the Correspondence-by-Segmentation Hypothesis, which states that correspondence is primarily learned by first segmenting speech in an unsupervised manner and then mapping the acoustics of different speakers onto each other. We show that a rudimentary segmentation of speech can be learned in an unsupervised fashion. We then demonstrate that, using the previously learned segmentation, different instances of a word can be mapped onto each other with high accuracy when trained on utterance-label pairs for a small set of words.