Polish Emotional Speech Recognition Using Artifical Neural Network

Powroźnik, Paweł

doi:10.12913/22998624/562

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The third is the difference in classification models. Commonly used classification models are Support Vector Machine (SVM; Vapnik, 1999), Random Forest (RF; Svetnik et al, 2003), Hidden Markov Model (HMM; Lay et al, 2003), Gaussian Mixture Model (GMM; Navyasri et al, 2018), Artifical Neural Network (ANN; Powroznik, 2014) and the Soft-max regression classifier widely used in deep learning (Liu and Zheng, 2007).…”

Section: Introductionmentioning

confidence: 99%

A Novel User Emotional Interaction Design Model Using Long and Short-Term Memory Networks and Deep Learning

Chen

Huang

et al. 2021

Front. Psychol.

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Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient (MFCC) of the speech signal is used as the input of the improved long and short-term memory network (ILSTM). To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed.

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Section: Introductionmentioning

confidence: 99%

A Novel User Emotional Interaction Design Model Using Long and Short-Term Memory Networks and Deep Learning

Chen

Huang

et al. 2021

Front. Psychol.

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“…Regression modelling is one of the most often used methods to solve problems in engineering, economics and management science [10,17,18]. For instance, mathematical models are utilized to characterize the relationship and to predict possible fault patterns based on the process conditions in the spot welding process [14] and to find an input-output relationship in a tungsten inert gas welding process [7].…”

Section: Introductionmentioning

confidence: 99%

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

Lemu¹,

Trzepieciński²,

Fejkiel³

2017

Adv. Sci. Technol. Res. J.

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This article reports a proposed approach to a frictional resistance description in sheet metal forming processes that enables the determination of the friction coefficient value under a wide range of friction conditions, without performing timeconsuming experiments. The motivation for this proposal is the fact that there exists a considerable amount of factors that affect the friction coefficient value and as a result building analytical friction model for specified process conditions is practically impossible. In this proposed approach, a mathematical model of friction behaviour is created using multiple regression analysis and artificial neural networks. The regression analysis was performed using a subroutine in MATLAB programming code and STATISTICA Neural Networks was utilized to build an artificial neural networks model. The effect of different training strategies on the quality of neural networks was studied. As input variables for regression model and training of radial basis function networks, generalized regression neural networks and multilayer networks, the results of strip drawing friction test were utilized. Four kinds of Al-Mg alloy sheets were used as a test material.

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“…Obviously, information which is conveyed by voice intonation have more then only textual meaning. The same sentences pronounced with different emotions can have completely different meaning [15].…”

Section: Introductionmentioning

confidence: 99%

Spectral Methods in Polish Emotional Speech Recognition

Powroźnik¹,

Czerwiński²

2016

Adv. Sci. Technol. Res. J.

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In this article the issue of emotion recognition based on Polish emotional speech signal analysis was presented. The Polish database of emotional speech, prepared and shared by the Medical Electronics Division of the Lodz University of Technology, has been used for research. Speech signal has been processed by Artificial Neural Networks (ANN). The inputs for ANN were information obtained from signal spectrogram. Researches were conducted for three different spectrogram divisions. The ANN consists of four layers but the number of neurons in each layer depends of spectrogram division. Conducted researches focused on six emotional states: a neutral state, sadness, joy, anger, fear and boredom. The averange effectiveness of emotions recognition was about 80%.

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Polish Emotional Speech Recognition Using Artifical Neural Network

Cited by 5 publications

References 8 publications

A Novel User Emotional Interaction Design Model Using Long and Short-Term Memory Networks and Deep Learning

A Novel User Emotional Interaction Design Model Using Long and Short-Term Memory Networks and Deep Learning

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

Spectral Methods in Polish Emotional Speech Recognition

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