Speech Emotion Recognition Technology Research Articles

The Impact of Fear on the Playcount of Horror Audiobooks

With innovations in the technology of smartphones in recent years, audiobooks have become increasingly popular, especially in times of coronavirus pandemics. Does the emotion in the audio have a positive effect on the playcount? Previous studies have addressed this issue primarily by considering emotional expression as static and attending to the overall or average level of expressed emotion. We analyzed data in 271 audios, using the speech emotion recognition technology. The discoveries shed light on the advantages of a higher density of fear. Moreover, the maximum duration of fear in audio has a positive relationship with playcount.

Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information

Identifying a person’s emotions is an important element in communication. In particular, voice is a means of communication for easily and naturally expressing emotions. Speech emotion recognition technology is a crucial component of human–computer interaction (HCI), in which accurately identifying emotions is key. Therefore, this study presents a two-stream-based emotion recognition model based on bidirectional long short-term memory (Bi-LSTM) and convolutional neural networks (CNNs) using a Korean speech emotion database, and the performance is comparatively analyzed. The data used in the experiment were obtained from the Korean speech emotion recognition database built by Chosun University. Two deep learning models, Bi-LSTM and YAMNet, which is a CNN-based transfer learning model, were connected in a two-stream architecture to design an emotion recognition model. Various speech feature extraction methods and deep learning models were compared in terms of performance. Consequently, the speech emotion recognition performance of Bi-LSTM and YAMNet was 90.38% and 94.91%, respectively. However, the performance of the two-stream model was 96%, which was a minimum of 1.09% and up to 5.62% improved compared with a single model.

Design of Efficient Speech Emotion Recognition Based on Multi Task Learning

Speech emotion recognition technology includes feature extraction and classifier construction. However, the recognition efficiency is reduced due to noise interference and gender differences. To solve this problem, this paper used two multi-task learning models based on adversarial multi-task learning(ASP-MTL). The first model took emotion recognition as the main task and noise recognition as the auxiliary task, and removed the noise part identified by the auxiliary task. After identifying the non-noise part, the second model was constructed. The second model took emotion recognition as the main task and gender classification as the auxiliary task. These two multi-task learning models can not only can use shared information to learn the relationship between different tasks, but also can identify specific tasks. This paper used Audio/Visual Emotion Challenge (AVEC) database and AFEW6.0 database,which were recorded in the field environment. Considering the problem of data imbalance between datasets, the data balance operation was carried out on the data sets in the process of data preprocessing. The paper shows an increase of around 10% in terms of accuracy and F1 score with the recent works using AVEC database and AFEW6.0 datasets, which proved that this paper has made a great progress in SER.

Emotional state analysis based on speech recognition signals

At present, as intelligent speech recognition is widely used, the importance of speech emotion recognition technology is also gradually gaining recognition. Considering the current social pressure, the generation of negative emotions has become particularly common. Therefore, it is necessary to increase the recognition of different emotions by using the speech recognition system, which has been perfected, so as to play a role in anticipation and prevention. In this paper, we focus on the negative effects of negative emotions on people's lives, and use K-nearest classifier to identify different emotions, and accurately identify them, and prevent and monitor them. Finally, the feasibility of this method is confirmed by simulating the process.

A novel speech emotion recognition method based on feature construction and ensemble learning.

In the field of Human-Computer Interaction (HCI), speech emotion recognition technology plays an important role. Facing a small number of speech emotion data, a novel speech emotion recognition method based on feature construction and ensemble learning is proposed in this paper. Firstly, the acoustic features are extracted from the speech signal and combined to form different original feature sets. Secondly, based on Light Gradient Boosting Machine (LightGBM) and Sequential Forward Selection (SFS) method, a novel feature selection method named L-SFS is proposed. And then, the softmax regression model is used to learn automatically the weights of the four single weak learners including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost) and LightGBM. Lastly, based on the learned automatically weights and the weighted average probability voting strategy, an ensemble classification model named Sklex is constructed, which integrates the above four single weak learners. In conclusion, the method reflects the effectiveness of feature construction and the superiority and stability of ensemble learning, and gets good speech emotion recognition accuracy.

The Application of Cloud Computing Service and Voicematic Identification Intelligence in the Supply Chain during the COVID-19 Pandemic

After the outbreak of the COVID-19 pandemic, cloud computing and voice recognition services have provided a more critical role in the enterprise supply chain management process. Speech emotion recognition technology can identify the content and emotion of the speaker according to the content and tone of speech. Analyzing the attitude and tone behind a statement through language and high-level information acquisition is very difficult. Cloud computing technology is becoming more widely used each day and is the cornerstone of enterprise information development. The anti-globalization caused by COVID-19 has made enterprises pay increasing attention to supply chain management. Determining how to optimize and integrate the supply chain has become an urgent problem for enterprises. Based on the work and research completed in this field, this paper first analyzes the algorithm and model used in speech recognition and then tests the system. To improve the utilization rate of enterprise resources and enterprise industrial benefits, this paper puts forward the optimization scheme of the enterprise supply chain from the perspective infrastructure construction to product development to backstage management and procurement.

Research on Feature Fusion Speech Emotion Recognition Technology for Smart Teaching

At present, there are numerous intelligent teaching applications based on various advanced pattern recognition technologies, such as face sign-in, classroom action recognition, student facial expression recognition, and other systems which have been gradually applied to major schools. Speech emotion recognition can analyze the characteristics of current teaching emotions, so as to discover the rules and details of teachers’ grasp of emotions. In order to realize the task of emotion classification in the process of intelligent teaching through machine learning technology, a teaching speech emotion recognition method with multifeature fusion and deep learning is proposed. The proposed method front-end processes the speech spectrum features in a multifeature fusion manner and combines them with an artificial neural network classifier to form the final speech emotion recognition model. First, after speech preprocessing, three features are selected and fused features using a network structure trained by parallel subnetworks. Then, the classifier used is a hybrid neural network classifier combining a convolutional neural network and a recurrent neural network. Finally, 100 open web courses were used to train and test the model. The test results show that the hybrid neural network sound model using multifeature fusion has good teaching speech emotion recognition capability.

Intelligent Aging Home Control Method and System for Internet of Things Emotion Recognition.

To solve a series of pension problems caused by aging, based on the emotional recognition of the Internet of Things, the control method and system research of smart homes are proposed. This article makes a detailed analysis and research on the necessity, feasibility, and how to realize speech emotion recognition technology in smart families, introduces the definition and classification of emotion, and puts forward five main emotions to be recognized in speech emotion recognition based on smart family environment. Then, based on this, it analyses the acquisition methods of emotional speech data. On this premise, this article discusses and analyses the related problems of voice data acquisition in smart homes, such as the voice characteristics and acquisition methods, puts forward three rules for voice text design, and determines the relatively suitable hybrid recording acquisition method applied in a smart home environment. At the same time, the design and establishment process of intelligent family emotional speech database is described in detail. The related problems of feature extraction in speech emotion recognition are studied. Starting from the definition of feature extraction, this article expounds on the necessity of feature extraction in the process of recognition and analyses the characteristics of the speech signals. For the specific environment of the smart family, the speech signal required to be processed needs to be close to the auditory characteristics of the human ears, and the speech signal contains enough information. Finally, the Mel frequency cepstrum coefficient (MFCC) is selected as the feature parameter applied in this article, and the extraction process of MFCC is introduced in detail.

Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition

In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care. The combination of speech recognition and speech emotion recognition can improve the feedback efficiency and the quality of service. Thus, the speech emotion recognition has been attracted much attention in both industry and academic. Since emotions existing in an entire utterance may have varied probabilities, speech emotion is likely to be ambiguous, which poses great challenges to recognition tasks. However, previous studies commonly assigned a single-label or multi-label to each utterance in certain. Therefore, their algorithms result in low accuracies because of the inappropriate representation. Inspired by the optimally interacting theory, we address the ambiguous speech emotions by proposing a novel multi-classifier interactive learning (MCIL) method. In MCIL, multiple different classifiers first mimic several individuals, who have inconsistent cognitions of ambiguous emotions, and construct new ambiguous labels (the emotion probability distribution). Then, they are retrained with the new labels to interact with their cognitions. This procedure enables each classifier to learn better representations of ambiguous data from others, and further improves the recognition ability. The experiments on three benchmark corpora (MAS, IEMOCAP, and FAU-AIBO) demonstrate that MCIL does not only improve each classifier's performance, but also raises their recognition consistency from moderate to substantial.

Song Recommendation Application Using Speech Emotion Recognition

This research was conducted to facilitate the interaction between radio broadcasters and radio listeners during the song request process. This research was triggered by the difficulty of the broadcasters in monitoring song requests from listeners. The system is made to accommodate all song requests by listeners. The application produced in this study uses speech emotion recognition technology based on a person's mood obtained from the spoken words. This technology can change the voice into one of the mood categories: neutral, angry, sad, and afraid. The k-Nearest Neighbor method is used to get recommendations for recommended song titles by looking for the closeness of the value between the listener's mood and the availability of song playlists. kNN is used because this method is suitable for user-based collaborative problems. kNN will recommend three songs which then be offered to listeners by broadcasters. Based on tests conducted to the broadcasters and radio listeners, this study has produced a song request application by recommending song titles according to the listener's mood, the text message, the searching songs, and the song requests and the song details that have been requested. Functional test that has been carried out has received 100 because all test components have succeeded as expected.

A Comparative Study On Speech Emotion Recognition

Today's world has been "Chatting" with the machines for a long time. With the first known research paper by Daellert et. al on this topic, we can say that discussions on Speech Emotion Recognition technology have been there for such a long time and have been evolving and increasing its applications in our life. Although worthy of these many applications, speech emotion recognition is a challenging task as emotion is a subjective thing. Not all humans are the same, each human deals differently with emotions. There are no common criteria or steps to categorize emotions. Forget computers, even we humans at times fail to read the emotions behind the other person. This paper provides the list of some speech emotion recognition methods and a glimpse of method used.