Abstract
In this paper we investigate methods to adapt a system for filled pause (FP) disfluency removal to different data properties. A gradient descent algorithm for parameter optimization is presented which achieves 80.6% recall and 87.7% precision on the FP dataset and 46.5% recall and 79.6% precision on the FPElo dataset. This compares to the results produced with hand-optimization on the test set. Furthermore we investigated the impact of cross-validation and training set selection on recognizer output in order to improve the speech retrieval system.
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Hamzah, R., Jamil, N., Seman, N. (2014). Nurturing Filled Pause Detection for Spontaneous Speech Retrieval. In: Jaafar, A., et al. Information Retrieval Technology. AIRS 2014. Lecture Notes in Computer Science, vol 8870. Springer, Cham. https://doi.org/10.1007/978-3-319-12844-3_39
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DOI: https://doi.org/10.1007/978-3-319-12844-3_39
Publisher Name: Springer, Cham
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