Abstract
In this paper, enhancements of online speech activity detection (SAD) is presented. Our proposed approach combines standard signal processing methods and modern deep-learning methods which allows simultaneous training of the detector’s parts that are usually trained or designed separately. In our SAD, an NN-based early score computation system, an NN-based score smoothing system and proposed online decoding system were incorporated in a training process. Besides the CNN and DNN, spectral flux and spectral variance features are also investigated. The proposed approach was tested on a Czech Radio broadcasting corpus. The corpus was used for investigation supervised and also semi-supervised machine learning.
J. Zelinka—This work was supported by the European Regional Development Fund under the project AI&Reasoning (reg. no. CZ.02.1.01/0.0/0.0/15 003/0000466).
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Zelinka, J. (2018). Deep Learning and Online Speech Activity Detection for Czech Radio Broadcasting. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_46
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DOI: https://doi.org/10.1007/978-3-030-00794-2_46
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