Efficient Emotion Recognition from Speech Using Deep Learning on Spectrograms
By Aharon Satt, Shai Rozenberg, Ron Hoory. Interspeech, 2017
379 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Signal Processing
View Paper: https://www.semanticscholar.org/paper/de47fc09bc8dcd032c8b3450a0b2a816c376e07e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
In Experimental and Cognitive Psychology
- Leonardo Pepino
- Luciana Ferrer
- Seunghyun Yoon
- Edward Jones
- Thamer Alhussain
- Kyomin Jung
- Seokhyun Byun
- M. I. Babar
In Signal Processing
- Leonardo Pepino
- Luciana Ferrer
- Seunghyun Yoon
- Edward Jones
- Thamer Alhussain
- Kyomin Jung
- Seokhyun Byun
- M. I. Babar
Citations in the corpus
- Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings (2021, methodology): “In terms of modeling techniques, many different traditional approaches have been proposed, using hidden Markov models (HMMs) [7, 8], support vector machines (SVMs) [9, 10], decision trees [11, 12], and, more recently, deep neural networks (DNNs) [13, 14, 15].”
- Multimodal Speech Emotion Recognition Using Audio and Text (2018, methodology): “These neural network-based models are combined to produce higher-complexity models [19, 20], and these models achieved the best-recorded performance when applied to the IEMOCAP dataset.”
- Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, background): “and fertile ground for future research opportunities not only in SER but many other domains [85]–[87].”