A Review on Emotion Recognition Algorithms using Speech Analysis <s> Artificial Neural Network (ANN) and Deep Neural Network (DNN) <s> This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition. <s> BIB001 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Artificial Neural Network (ANN) and Deep Neural Network (DNN) <s> Considering the application value of emotion, increasing attention has been attracted on emotion recognition over the last decades. We devote ourselves to feasible speech emotion recognition research. We build two affective model based on deep learning methods (stacked autoencoder network, deep belief network) for automatic salient emotion feature extraction, emotion states classification. The experiments are based on a well-known German Berlin Emotional Speech Database,, the recognition accuracy reaches 65% in the best case. In addition, we validate the influence of different speakers, different emotion categories on recognition accuracy. <s> BIB002
The term artificial neuron network (ANN) is a term commonly used for a system that imitates the flow of the neuron. Information is received from the input and flows from one node to another, until it reaches the output. Through this process, the system will learn about the input given. Three branches of ANN will be discussed, including feedforward neural network, deep neural network and convolutional neural network. The feedforward neural network is the first type of neural network developed. The process is the most basic one of all: the data is forwarded through an input layer to a single hidden layer, then to the output layer. In feedfoward, there are no loops or cycles. In the feedforward neural network, there is the input layer, a hidden layer, and an output layer. A deep neural network expands the possibilities by adding more layers in the hidden layer segment BIB001 BIB002 . An interesting characteristic of DNNs is that they can learn high-level invariant features from raw data. Convolutional neural network (CNN), as shown in Fig. 4 , is inspired by the visual cortex, where cells are activated according to their sub-regions. Applying that to ANN, the CNN information in the neurons are connected to their sub-regions first, before passing to the next layer. Some sub-regions may overlap. This contrasts with other neural network architectures where each neuron is independent  . While CNNs are highly sophisticated and can be used for SER, it is specifically suitable for image processing and recognition, due to the convolutional layer. The classifier is the algorithm that determines how these features are manipulated and translated into emotion recognition. Common classifiers are HMM, SVM, GMM, and ANN. DNN is used, a more sophisticated version of feedforward ANN. Table 2 shows that ANN boosts deep potential for pattern recognition, provided that more layers are supplied. The weakness of inconvenience when adding emotion can be simply solved by consolidating all initial parameters at the start. This claim is further supported in Table 3 , where using DNN may generate more accurate recognition compared to other classifiers. 