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Segment boundary detection via class entropy measurements in connectionist phoneme recognition
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.ORCID iD: 0000-0002-3323-5311
2006 (English)In: Speech Communication, ISSN 0167-6393, Vol. 48, no 12, 1666-1676 p.Article in journal (Refereed) Published
Abstract [en]

This article investigates the possibility to use the class entropy of the output of a connectionist phoneme recogniser to predict time boundaries between phonetic classes. The rationale is that the value of the entropy should increase in proximity of a transition between two segments that are well modelled (known) by the recognition network since it is a measure of uncertainty. The advantage of this measure is its simplicity as the posterior probabilities of each class are available in connectionist phoneme recognition.The entropy and a number of measures based on differentiation of the entropy are used in isolation and in combination. The decision methods for predicting the boundaries range from simple thresholds to neural network based procedure.The different methods are compared with respect to their precision, measured in terms of the ratio between the number C of predicted boundaries within 10 or 20 ms of the reference and the total number of predicted boundaries, and recall, measured as the ratio between C and the total number of reference boundaries.

Place, publisher, year, edition, pages
2006. Vol. 48, no 12, 1666-1676 p.
Keyword [en]
boundary detection, entropy, connectionist phoneme recognition, speech recognition
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-16250DOI: 10.1016/j.specom.2006.07.009ISI: 000243246600006ScopusID: 2-s2.0-37849185609OAI: diva2:334292
International Conference on Non-Linear Speech Processing. Barcelona, SPAIN. APR 19-22, 2005

QC 20100525, QC 20110929

Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2013-09-12Bibliographically approved

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Salvi, Giampiero
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