Emotion recognition from Marathi speech database using adaptive artificial neural network

Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models

“…A black dot (•) in a cell means the corresponding database was used in the research mentioned at the bottom of the column. Year 2005 2010 2011 2013 2014 2016 2017 2018 2019 2020 Research HMM, SVM [6] SVM [17] GerDA, RBM [22] LSTM, BLSTM [28] CRF, CRBM [24] SVM, PCA, LPP, TSL [90] DNN, ANN, ELM [23] DCNN, LSTM [29] CNN [21] DCNN [26] LSTM, MTL [33] ANN, PSOF [19] DCNN, DTPM, TSL [25] LSTM, VAE [31] GAN [86] GAN, SVM [88] LSTM, ATTN [94] DCNN, LSTM [30] CNN, VAE, DAE, AAE, AVB [32] DCNN, GAN [89] LDA, TSL, TLSL [91] CNN, BLSTM, ATTN, MTL [95] LSTM, ATTN [83] DNN, Generative [76] DCNN [79] Additionally, Figure 2a shows a comparison between accuracies reported in deep learning methods based on EMO-DB versus IEMOCAP, which we can see there is a clear separation between the accuracies published. Again, one reason could be the fact that EMO-DB has one degree of magnitude fewer number of samples than IEMOCAP, and using it with deep learning methods makes it more prone to overfitting.…”

“…Later in 2018, Darekar and Dhande [ 19 ] have introduced a system based on artificial neural networks, the first extract NMF analysis, Pitch Analysis, and Cepstrum features; then, they reduce their dimensionality applying PCA to their feature vectors. They then feed their features to an artificial neural network introduced by Bhatnagar and Gupta 2017 [ 20 ], called NARX Double Layer, which is an ANN with two hidden layers.…”

“…However, lately, by the development of deep learning tools and processes, solutions for SER can be changed as well. There is a lot of effort and research on employing these algorithms to recognize emotions from the speech [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. In addition to deep learning, more recently, along with improvements in recurrent neural networks and the use of long short-term memory (LSTM) networks, autoencoders, and generative adversarial models, there has been a wave of studies on SER using these techniques [ 28 , 29 , 30 , 31 , 32 , 33 ] to solve the problem.…”

Evaluating aircraft cockpit emotion through a neural network approach

“…Moreover, the application of neural networks to learn abstract features can avoid manual extraction of features and have local feature abstraction and memory functions. With the development of the artificial intelligence technology, scholars applied the neural network scheme through diverse methods such as through sound (Darekar and Dhande, 2018), facial expression (Jaina et al ., 2019), brain signal (Meza-Kubo et al ., 2016), and the vision (Ruwa et al ., 2019) methods, to evaluate the emotional cognition. Currently, different neural network models, including the back-propagation (BP) neural network, Hopfield neural network, adaptive resonance theory (ART) neural network, and the Kohonen neural network, are used for the emotional prediction evaluation.…”

A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

“…In order to address the abovementioned problems and improve the emotion recognition performance, many scholars at home and abroad have studied the recognition algorithms of speech emotion information which are based on the feature learning, and proposed a lot of effective recognition models [6,7]. Literature [8] proposes a multi-level acoustic feature fusion algorithm based on multiple kernel learning. And on this basis, the transfer multiple kernel learning algorithm based multi-level feature fusion is proposed, which can improve the recognition robustness of the feature in the training and the test dataset.…”