Abstract
In this paper we analyze different labeling strategies and their impact on speaker change detection rates. We explore binary, linear fuzzy, quadratic and Gaussian labeling functions. We come to the conclusion that the labeling function is very important and the linear variant outperforms the rest. We also add phase information from the spectrum to the input of our convolutional neural network. Experiments show that even though the phase is informative its benefit is negligible and may be omitted. In the experiments we use a coverage-purity measure which is independent on tolerance parameters.
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Acknowledgment
This research was supported by the Grand Agency of the Czech Republic, project No. P103/12/G084. We would also like to thank the grant of the University of West Bohemia, project No. SGS-2016-039. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.
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Hrúz, M., Salajka, P. (2017). Phase Analysis and Labeling Strategies in a CNN-Based Speaker Change Detection System. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_61
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DOI: https://doi.org/10.1007/978-3-319-66429-3_61
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