Learning with noisy supervision for Spoken Language Understanding

Data-driven spoken language understanding (SLU) systems need semantically annotated data which are expensive, time consuming and prone to human errors. Active learning has been successfully applied to automatic speech recognition and utterance classification. In general, corpora annotation for SLU i...

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Veröffentlicht in:	2008 IEEE International Conference on Acoustics, Speech and Signal Processing S. 4989 - 4992
Hauptverfasser:	Raymond, C., Riccardi, G.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.03.2008
Schlagworte:	Active Learning Conditional Random Fields Costs Data mining Humans Labeling Machine learning algorithms Natural languages Noise level Noise robustness Spoken Language Understanding Uncertainty
ISBN:	9781424414833, 1424414830
ISSN:	1520-6149
Online-Zugang:	Volltext
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