Convolutional and Deep Neural Networks based techniques for extracting the age-relevant features of the speaker
Author:
Funder
RUSA – Rashtriya Uchchatar Shiksha Abhiyan - BEICH 2.0
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s12652-021-03238-1.pdf
Reference47 articles.
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2. Abdel-Hamid O, Mohamed A-R, Jiang H, Deng Li, Penn G, Yu D (2014) Convolutional neural networks for speech recognition. IEEE/ACM Trans Audio Speech Lang Process. https://doi.org/10.1109/TASLP.2014.2339736
3. Anzalone L, Barra P, Barra S, Narducci F, Nappi M (2019) Transfer Learning for Facial Attributes Prediction and Clustering. In: Wang G, El Saddik A, Lai X, Martinez Perez G, Choo KK (Eds.). Smart City and Informatization iSCI 2019. Communications in Computer and Information Science. 1122. Springer, Singapore. Doi: https://doi.org/10.1007/978-981-15-1301-5_9.
4. Bachate RP, Sharma A (2019) Automatic speech recognition systems for regional languages in India. Int J Recent Technol Eng (IJRTE) 8(2S3):585–592. https://doi.org/10.35940/ijrte.B1108.0782S319
5. Barra P, Bisogni C, Nappi M, Freire-Obregón D, Castrillón Santana M (2019) Gait analysis for gender classification in forensics. Depend Sens Cloud Big Data Syst. https://doi.org/10.1007/978-981-15-1304-6_15
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