Speech emotion recognition using deep neural network and extreme learning machine

By Kun Han, Dong Yu, Ivan J. Tashev. Interspeech, 2014

745 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/40e74875bcb2d57c77607cc16daf9d07dd635dc0

How researchers see this paper

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

In Artificial Intelligence

In Experimental and Cognitive Psychology

Citations in the corpus

Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching (2018, methodology): “For instance, in [12], [13] a DNN is used”
Multimodal Language Analysis with Recurrent Multistage Fusion (2018, background): “, 2011; Cambria, 2016), and emotion recognition (Han et al., 2014; Bertero et al., 2016; Lakomkin et al., 2018).”
High-level feature representation using recurrent neural network for speech emotion recognition (2015, methodology): “The first four bars indicate the performance of the DNNbased systems described in [4].”
End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017, methodology): “[18] uses hand-crafted features to feed a DNN that pro-”
Interactive Text-to-Speech via Semi-supervised Style Transfer Learning (2020, methodology): “Recently, deep neural networks (DNNs) have first been used to learn high-level representations for utterance-level emotion recognition [20].”
A Survey on Deep Learning (2018, methodology): “SER relies heavily on the effectiveness of the speech features used 92:21 In [56], a newly developed ANN with one hidden layer, called the Extreme Learning Machine (ELM), is proposed for the utterance-level classification using DNNs.”
Didispeech: A Large Scale Mandarin Speech Corpus (2020, background): “Powered by the emerging technology on deep learning, rapid progress has been made on the key technologies in speech processing [1, 2], including automatic speech recognition (ASR) [3, 4, 5, 6], text-to-speech (TTS) [1, 2, 7], speaker identification (SID) [8, 9], speech emotion recognition [10, 11, 1”
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning (2018, background): “Speech emotion recognition, as an important aspect of emotion recognition, has shown tremendous progress under the development of deep learning [1-10].”
Multimodal Speech Emotion Recognition Using Audio and Text (2018, background): “An initial study utilized deep neural networks (DNNs) to extract high-level features from raw audio data and demonstrated its effectiveness in speech emotion recognition [15].”
Automatic speech emotion recognition using recurrent neural networks with local attention (2017, methodology): “However, works in [7] and [8] have both applied ELM for the utterance level aggregation, despite the fact that they already adopted a CTC-style recurrent network underneath [8].”
3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition (2018, methodology): “1) DNN-extreme learning machine (DNN-ELM) ( see [3]).”
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network (2019, background): “Emotion recognition can be performed using different features, such as facial expressions [2,9,10], speech [5,11], EEG [12], and even text [13].”
DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning (2015, background): “[36], for example, infers emotion from speech using deep learning but only to assist in feature selection – it remains uninvolved in clas-siﬁcation.”