72‐3: Fully Convolutional Transformer‐Based Speech Emotion Recognition for Automotive Systems

The acoustic cues that convey emotion in speech are similar to those that convey emotion in music, and recognition of emotion in both of these types of cue recruits overlapping networks in the brain. Given the similarities between music and speech prosody, developmental research is uniquely positioned to determine whether recognition of these cues develops in parallel. In the present study, we asked 60 children aged 6 to 11 years, and 51 university students, to judge the emotions of 10 musical excerpts, 10 inflected speech clips, and 10 affect burst clips. We presented stimuli intended to convey happiness, sadness, anger, fear, and pride. Each emotion was presented twice per type of stimulus. We found that recognition of emotions in music and speech developed in parallel, and adult-levels of recognition develop later for these stimuli than for affect bursts. We also found that sad stimuli were most easily recognised, followed by happiness, fear, and then anger. In addition, we found that recognition of emotion in speech and affect bursts can predict emotion recognition in music stimuli independently of age and musical training. Finally, although proud speech and affect bursts were not well recognised, children aged eight years and older showed adult-like responses in recognition of proud music.

Speech emotion recognition (SER) is an important field of study because we need better ways for computers and people to communicate. People’s feelings play a significant role in their conversations. It could be useful for machines to recognize and respond to human emotions in various areas, such as healthcare, education, customer service, and virtual assistants. Deep learning technologies have made significant strides in recent years, improving the performance of systems that handle speech-related tasks. SER has also seen major advancements due to the development of complex neural network designs. This paper offers an in-depth look at current research in SER, focusing on studies that use deep learning techniques. It compares the most effective models, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, transformers, and hybrid models. This study also talks about the most important things that affect SER performance, such as choosing speech emotion datasets and features. This review additionally discusses about the problems and limitations that current SER research faces. For example, datasets are not very diverse, emotions are hard to represent, speakers and languages are dependent on each other, and it is hard to use the technology in real time. This paper seeks to provide substantial insights for researchers on SER by synthesizing findings from multiple recent studies.

For speech emotion recognition, emotional feature set with high dimension may produce redundant features and influence the recognition accuracy. To solve this problem and obtain the optimal emotional feature subset of speech, a feature dimension reduction based on linear discriminant analysis is proposed. According to the confusion degree between different basic emotions, an emotion recognition method based on support vector machine decision tree is proposed. Experiment on speaker-dependent speech emotion recognition using Chinese speech database from institute of automation of Chinese academy of sciences is performed and a speech emotion recognition system is presented, where standard feature sets of the INTER-SPEECH and classic classifiers are used in comparative experiments respectively. Experimental results show that the proposal achieves 84.39% recognition accuracy on average. By proposal, it would be fast and efficient to discriminate emotional states of diverse speakers from speech, and it would make it possible to realize the interaction between speaker and computer/robot in the future.

Speech emotion recognition is a critical component for achieving natural human–robot interaction. The modulation-filtered cochleagram is a feature based on auditory modulation perception, which contains multi-dimensional spectral–temporal modulation representation. In this study, we propose an emotion recognition framework that utilizes a multi-level attention network to extract high-level emotional feature representations from the modulation-filtered cochleagram. Our approach utilizes channel-level attention and spatial-level attention modules to generate emotional saliency maps of channel and spatial feature representations, capturing significant emotional channel and feature space from the 3D convolution feature maps, respectively. Furthermore, we employ a temporal-level attention module to capture significant emotional regions from the concatenated feature sequence of the emotional saliency maps. Our experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset demonstrate that the modulation-filtered cochleagram significantly improves the prediction performance of categorical emotion compared to other evaluated features. Moreover, our emotion recognition framework achieves comparable unweighted accuracy of 71% in categorical emotion recognition by comparing with several existing approaches. In summary, our study demonstrates the effectiveness of the modulation-filtered cochleagram in speech emotion recognition, and our proposed multi-level attention framework provides a promising direction for future research in this field.

Speech emotion recognition (SER) is a hot topic in academia. One of the key issues in improving the performance of SER systems is the choice of speech emotion features. In order to establish a robust speech emotion recognition system, it is essential to select the features which can be a perfect representation of speech emotion attributes. Researchers has done a lot of work, proposed a variety of emotional features and made great progress. Although each kind of features were proven to be effective, most of methods are based on a single type. In this paper, we proposed a method of feature fusion based on deep learning, combining spectral-based features and pitch-based hyper-prosodic features. The experiments show that this method improves the performance of speech emotion recognition system.

There has been a lot of progress in recent years in the fields of expert systems, artificial intelligence (AI) and human machine interface (HMI). The use of voice commands to engage with machinery or instruct it to do a certain task is becoming more common. Numerous consumer electronics have SIRI, Alexa, Cortana, and Google Assistant built in. In the field of human-device interaction, emotion recognition from speech is a complex research subject. We can't imagine modern life without machines, so naturally there's a need to create a more robust framework for human-machine communication. A number of academics are now working on speech emotion recognition (SER) in an effort to improve the interaction between humans and machines. We aimed to identify four fundamental emotions: angry, unhappy, neutral and joyful from speech in our experiment. As you can hear below, we trained and tested our model using audio data of brief Manipuri speeches taken from films. This task makes use of convolutional neural networks (CNNs) to extract functions from speech in order to recognize different moods using the Mel-frequency cepstral coefficient (MFCC).

The history of humanity has reached Industry 4.0 that aims to the integration of information technologies and especially artificial intelligence with all life-sustaining mechanisms in the 21st century, and consecutively, the transformation of Society 5.0 has begun. Society 5.0 means a smart society in which humans share life with physical robots and software robots as well as smart devices based on augmented reality. Industry 4.0 contains main structures such as the internet of things, big data analytics, digital transformation, cyber-physical systems, artificial intelligence, and business processes optimization. It is impossible to consider the machines to be without emotions and emotional intelligence within the transformation of smart tools and artificial intelligence, in addition, while it is planned to give most of the commands with voice and speaking, it became more important to develop algorithms that can detect emotions. In the smart society, new and rapid methods are needed for speech recognition, emotion recognition, and speech emotion recognition areas to maximize human-computer (HCI) or human-robot interaction (HRI) and collaboration. In this study, speech recognition and speech emotion recognition studies in robot technology are investigated and developments are revealed.

Identification of emotions along with the semantics from the speech signals is still one of the most challenging tasks because of the arising difficulty in the extraction and selection of the appropriate emotion features. The speech emotion recognition (SER) system has become one of the active research topics in recent years. Many researchers proposed various models to enhance the machine responses during the man–machine interactions by identifying the emotional state of the customer. This research work aims to propose a multi-class classification model to extract and classify the emotional state of the speaker into eight classes, namely happy, sad, surprised, disgust, neutral, calm, angry, and fearful. In this research work, two machine learning classifiers, namely k-nearest neighbors and random forest, are used along with the multi-layer perceptron classifier of deep learning for determining the emotional features from speech signals. The dataset for this research is obtained from the RAVDESS. Along with the audio speeches of 24 actors, the audio song dataset is also utilized for the better training of the proposed model. The performance of all three algorithms is compared and evaluated by using the confusion matrix and accuracy plots. The empirical results show that the KNN classification model of ML has performed better than other proposed algorithms with increased accuracy of 76.25%.

Egyptian Arabic speech emotion recognition using prosodic, spectral and wavelet features

Purpose of research.Emotions play one of the key roles in the regulation of human behaviour. Solving the problem of automatic recognition of emotions makes it possible to increase the effectiveness of operation of a whole range of digital systems such as security systems, human-machine interfaces, e-commerce systems, etc. At the same time, the low efficiency of modern approaches to recognizing emotions in speech can be noted. This work studies automatic recognition of emotions in speech applying machine learning methods.Methods.The article describes and tests an approach to automatic recognition of emotions in speech based on multitask learning of deep convolution neural networks of AlexNet and VGG architectures using automatic selection of the weight coefficients for each task when calculating the final loss value during learning. All the models were trained on a sample of the IEMOCAP dataset with four emotional categories of ‘anger’, ‘happiness’, ‘neutral emotion’, ‘sadness’. The log-mel spectrograms of statements processed by a specialized algorithm are used as input data.Results.The considered models were tested on the basis of numerical metrics: the share of correctly recognized instances, accuracy, completeness, f-measure. For all of the above metrics, an improvement in the quality of emotion recognition by the proposed model was obtained in comparison with the two basic single-task models as well as with known solutions. This result is achieved through the use of automatic weighting of the values of the loss functions from individual tasks when forming the final value of the error in the learning process.Conclusion.The resulting improvement in the quality of emotion recognition in comparison with the known solutions confirms the feasibility of applying multitask learning to increase the accuracy of emotion recognition models. The developed approach makes it possible to achieve a uniform and simultaneous reduction of errors of individual tasks, and is used in the field of emotions recognition in speech for the first time.

-- Speech signals are being considered as most effective means of communication between human beings. Many researchers have found different methods or systems to identify emotions from speech signals. Here, the various features of speech are used to classify emotions. Features like pitch, tone, intensity are essential for classification. Large number of the datasets are available for speech emotion recognition. Firstly, the extraction of features from speech emotion is carried out and then another important part is classification of emotions based upon speech. Hence, different classifiers are used to classify emotions such as Happy, Sad, Anger, Surprise, Neutral, etc. Although, there are other approaches based on machine learning algorithms for identifying emotions. Speech Emotion Recognition is a current research topic because of its wide range of applications and it became a challenge in the field of speech processing too. We have carried out a brief study on Speech Emotion Analysis along with Emotion Recognition. Speech Emotion Recognition (SER) can be defined as extraction of the emotional state of the speaker from his or her speech signal. There are few universal emotions including Neutral, Anger, we have worked on different tools to be used in SER. SER is tough because emotions are subjective and annotating audio is challenging task. Emotion recognition is the part of speech recognition which is gaining more popularity and need for it increases enormously. We have classified based on different type of emotions to detect from speech. Key Words: Speech Emotion Recognition, Affective Computing, Machine Learning, Deep Learning, Audio Signal Processing, Emotion Classification, Feature Extraction, Prosodic Features, Spectral Features, Mel-Frequency Cepstral Coefficients (MFCCs), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Attention Mechanisms, Multimodal Emotion Recognition, Speaker-Independent SER, Real-Time Emotion Detection, Noise-Robust Emotion Recognition, Data Augmentation, Emotion-Aware Applications

When we are speaking with someone, the first thing we will notice about them is their face. We can easily guess their emotional state by looking at their face, such as which feeling they are experiencing. When we're talking indirectly, however, we can't make the same prediction. As a result, a human may predict the feelings of another human being to some extent. However, a machine such as a computer is incapable of doing so. When we handed up The emotional state of a human being cannot be predicted using an audio clip as input to a computer. As a result, we devised a technique dubbed "Speech Emotion Recognition"[SER] to address this issue. Different strategies can be used to address this voice emotion recognition challenge, but the accuracy is what matters. So we solved this difficulty by comparing the accuracies of two technologies. Here, The most efficient technique for voice emotion detection is the convolution neural network algorithm, which uses several modules for emotion recognition and classifiers to discriminate emotions like happy, surprise, anger, neutral mood, sadness, and so on. [IN-DEE LEARNING] Emotions play a vital role in everyone's lives. Everyone wants to know what other people are up to. As if we can say what the other person thinks when we're talking directly. I'm referring about the facial expressions as well as the tone of voice. However, when it comes to phone calls, we don't know and can't forecast what the person is thinking as an emotion, thus to fix this, we've turned to a new trend known as "Speech Emotion Recognition"[SER]. So, in order to tackle this problem, we used the Machine Learning Language, which includes an efficient algorithm known as SVM, as well as the database Ravdess[Machine Learning].

Unsupervised feature selection and NMF de-noising for robust Speech Emotion Recognition

Speech emotion recognition is a challenging and significant task. On the one hand, the emotion features need to be robust enough to capture the emotion information, and while on the other, machine learning algorithms need to be insensitive to model the utterance. In this paper, we presented a novel framework of speech emotion recognition to address the two above-mentioned challenges. Relative Entropy based Normalization (REN) was proposed to normalize the supervectors of Gaussian Mixture Model-Universal Background Model (GMM-UBM) as the features to emotions. The Kernel Extreme Learning Machine (KELM) was adopted as the classifier to identify the emotion represented by the normalized supervectors. Experimental results on the EMR_1309 corpus showed the proposed framework outperformed the state-of-the-art i-vector based systems.

Speech emotion recognition (SER) is one of the latest challenge in human-computer interaction. In conventional SER classification methods, a single emotion label is outputted per one utterance as the estimation result. This is because conventional speech emotional databases which are used to train SER models have a single emotion label for one utterance. However, it is often the case that multiple emotions are expressed simultaneously with different intensities in human speech. In order to realize more natural SER than ever, existence of multiple emotions in one utterance should be taken into account. Therefore, we created an emotional speech database which contains multiple emotions and their intensities labels. The creation experiment was conducted by extracting speech utterance parts where emotions appear from existing video works. In addition, we evaluated the created database by performing statistical analysis on the database. As a result, 2,025 samples were obtained, of which 1,525 samples contained multiple emotions.