Multi-task learning DNN to improve gender identification from speech leveraging age information of the speaker

Author:

Sarma Mousmita,Sarma Kandarpa Kumar,Goel Nagendra Kumar

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Reference34 articles.

1. Alhussein, M., Ali, Z., Imran, M., & Abdul, W. (2016). Automatic gender detection based on characteristics of vocal folds for mobile healthcare system, Mobile Information Systems, vol 2016, 1-12.

2. Cheng, G., Peddinti, V., Povey, D., Manohar, V., Khudanpur, S., Yan, Y. (2017). An exploration of dropout with LSTMs, in Proceedings of Interspeech 2017, The 11th Annual Conference of the International Speech Communication Association, August 20–24, Stockholm, Sweden.

3. Ghahremani, P., Manohar, V., Povey, D., & Khudanpur, S. (2016). Acoustic modelling from the signal domain using CNNs, In Proceedings of Interspeech 2016, 17th Annual Conference of the International Speech Communication Association, September 8–12, San Francisco, CA, USA.

4. Goel, N. K., Sarma, M., Kushwah, T. S., Agrawal, D. K., Iqbal, Z., & Chauhan, S. (2018). Extracting speaker’s gender, accent, age and emotional state from speech, In Proceedings of Interspeech 2018, The 19th Annual Conference of the International Speech Communication Association, (pp. 2–6). Hyderabad, India.

5. Golik, P., Tuske, Z., Schluter, R., & Ney, H. (2015). Convolutional Neural Networks for Acoustic Modeling of Raw Time Signal in LVCSR, In Proceedings of Interspeech 2015, The 16th Annual Conference of the International Speech Communication Association, (pp. 26–30). September 6–10, Dresden, Germany.

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