Speech emotion recognition using convolutional and Recurrent Neural Networks

By Wootaek Lim, Dae-young Jang, Taejin Lee. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2016

350 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Tensor Fusion Network for Multimodal Sentiment Analysis (2017, methodology): “SER-LSTM (Lim et al., 2016) is a model that uses recurrent neural networks on top of convolution operations on spectrogram of audio.”
A Comprehensive Review of Speech Emotion Recognition Systems (2021, methodology): “A concatenated CNN and RNN architecture is employed without utilizing the traditional hand-crafted features [78], and a time-distributed CNN network is proposed.”
Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, background): “The tendency of DNNs to learning the specific features from various auditory emotion recognition systems is analyzed in [117].”
An End-to-End Multimodal Voice Activity Detection Using WaveNet Encoder and Residual Networks (2019, background): “[22] proposed to transform the speech signal using a short-”
End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017, methodology): “[19] after transforming the data using short time Fourier transform, they used CNNs to extract high-level features.”
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph (2018, methodology): “For acoustic only baseline models: AdieuNet (Trigeorgis et al., 2016), SER-LSTM (Lim et al., 2016).”