Voice Activity Detection Using Convolutional Recurrent Neural Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47451-4_17
Reference19 articles.
1. Afroz, F., Koolagudi, S.G.: Recognition and classification of pauses in stuttered speech using acoustic features. In: 2019 6th International Conference on Signal Processing and Integrated Networks, SPIN 2019, pp. 921–926 (2019)
2. Agarwal, A.R., Tiwari, S., Patage, V.V., Sankar Ganesh, S., Sudhakar, M.S.: A method for voice activity detection using K-means clustering. In: 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022 (2022)
3. Lecture Notes in Computer Science;M Barthet,2011
4. Bhattacharjee, M., Prasanna, S.R.M., Guha, P.: Clean vs. overlapped speech-music detection using harmonic-percussive features and multi-task learning. IEEE/ACM Trans. Audio Speech Lang. Process. 31, 1–10 (2023)
5. Chung, J.S., Huh, J., Nagrani, A., Afouras, T., Zisserman, A.: Spot the conversation: speaker diarisation in the wild. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, October 2020, pp. 299–303 (2020)
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