Sparse Autoencoder-Based Speech Emotion Recognition
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-2130-8_42
Reference10 articles.
1. Juyal S, Killa C, Singh GP, Gupta N, Gupta V (2021) Emotion recognition from speech using deep neural network. In: Srivastava S, Khari M, Gonzalez Crespo R, Chaudhary G, Arora P (eds) Concepts and real-time applications of deep learning. EAI/Springer innovations in communication and computing. Springer, Cham. https://doi.org/10.1007/978-3-030-76167-7_1
2. Huang F, Zhang J, Zhou C et al (2020) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17:217–229. https://doi.org/10.1007%2Fs10346-019-01274-9
3. Khalil RA, Jones E, Babar MI, Jan T, Zafar MH, Alhussain T (2019) Speech emotion recognition using deep learning techniques: a review. IEEE Access 7:117327–117345. https://ieeexplore.ieee.org/document/8805181
4. Chollet F (2016) Building autoencoders. In: Keras. https://blog.keras.io/building-autoencoders-in-keras.html
5. Pravin SC, Palanivelan M (2021) A hybrid deep ensemble for speech disfluency classification. In: Circuits, systems, and signal processing, vol 40, No 8. Springer, Berlin, pp 3968–3995. https://www.researchgate.net/publication/349228170_A_Hybrid_Deep_Ensemble_for_Speech_Disfluency_Classification
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