Deep Learning and Online Speech Activity Detection for Czech Radio Broadcasting
Author:
Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-00794-2_46
Reference11 articles.
1. Chen, J., Wang, Y., Wang, D.: A feature study for classification-based speech separation at very low signal-to-noise ratio. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7039–7043, May 2014
2. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
3. Hughes, T., Mierle, K.: Recurrent neural networks for voice activity detection. In: ICASSP, pp. 7378–7382 (2013)
4. Lehner, B., Widmer, G., Sonnleitner, R.: On the reduction of false positives in singing voice detection. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7480–7484 (2014)
5. Mateju, L., Cerva, P., Zdansky, J., Malek, J.: Speech activity detection in online broadcast transcription using deep neural networks and weighted finite state transducers. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5460–5464, March 2017
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