Importance Sampling Of Delta-Auc: A Basis For Active Learning For Improved Keyword Search
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2016)
Abstract
We present an importance sampling based approach to the active learning problem of selecting additional training data to supplement a seed model. Our proposed. Delta-AUC selection optimizes AUC improvement in keyword search and is evaluated on the Spanish Fisher corpus. We show that over different training data sizes, Delta-AUC selection consistently outperforms random sampling by 1.05% to 2.69% absolute AUC and requires no more than 60% of the transcriptions needed by random sampling to achieve the same AUC. On terms not seen in the original seed model training, the proposed algorithm achieves a 3.47% better AUC and 4.66% reduction in word error rate. We also introduce a regression analysis model that can refine our Delta-AUC strategy in the future.
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Key words
Active learning,CTS,STT,KWS
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