Unsupervised Segmentation of Audio Speech Using the Voting Experts Algorithm

DOI

10.2991/agi.2009.25How to use a DOI?

Abstract

Human beings have an apparently innate ability to seg- ment continuous audio speech into words, and that abil- ity is present in infants as young as 8 months old. This propensity towards audio segmentation seems to lay the groundwork for language learning. To artificially repro- duce this ability would be both practically useful and theoretically enlightening. In this paper we propose an algorithm for the unsupervised segmentation of audio speech, based on the Voting Experts (VE) algorithm, which was originally designed to segment sequences of discrete tokens into categorical episodes. We demon- strate that our procedure is capable of inducing breaks with an accuracy substantially greater than chance, and suggest possible avenues of exploration to further in- crease the segmentation quality.

Copyright

© 2009, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Matthew Miller
AU  - Peter Wong
AU  - Alexander Stoytchev
PY  - 2009/06
DA  - 2009/06
TI  - Unsupervised Segmentation of Audio Speech Using the Voting Experts Algorithm
BT  - Proceedings of the 2nd Conference on Artificial General Intelligence (2009)
PB  - Atlantis Press
SP  - 108
EP  - 113
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2009.25
DO  - 10.2991/agi.2009.25
ID  - Miller2009/06
ER  -