Emotion Recognition of Affective Speech Based on Multiple Classifiers Using Acoustic-Prosodic Information and Semantic Labels

The speech signal consists of linguistic information and also paralinguistic one such as emotion. The modern automatic speech recognition systems have achieved high performance in neutral style speech recognition, but they cannot maintain their high recognition rate for spontaneous speech. So, emotion recognition is an important step toward emotional speech recognition. The accuracy of an emotion recognition system is dependent on different factors such as the type and number of emotional states and selected features, and also the type of classifier. In this paper, a modular neural-support vector machine (SVM) classifier is proposed, and its performance in emotion recognition is compared to Gaussian mixture model, multi-layer perceptron neural network, and C5.0-based classifiers. The most efficient features are also selected by using the analysis of variations method. It is noted that the proposed modular scheme is achieved through a comparative study of different features and characteristics of an individual emotional state with the aim of improving the recognition performance. Empirical results show that even by discarding 22% of features, the average emotion recognition accuracy can be improved by 2.2%. Also, the proposed modular neural-SVM classifier improves the recognition accuracy at least by 8% as compared to the simulated monolithic classifiers.

Context-aware computing has become an essential component of modern wearable technology, enabling devices to sense, interpret, and respond to users’ environments and affective states. By integrating contextual intelligence, smart systems can adapt their own behavior to better align with user needs, enhancing efficiency, personalization, and user experience. A critical aspect of context awareness is the ability to recognize a user’s internal state, particularly emotions, which influence decision-making, communication, and overall well-being. While emotion recognition is traditionally approached through multimodal sensing—using cameras, microphones, or physiological sensors such as Electroencephalography (EEG), Electrocardiography (ECG), and Electro-dermal Activity (EDA)—these methods often require multiple sensors, leading to challenges related to power consumption, complexity, and privacy. The demand for a more efficient and unobtrusive approach to emotion and context recognition has driven research toward minimal-sensor solutions that can extract meaningful affective and contextual information with reduced hardware requirements. Restricting sensing sources of contextual information by using only limited number of sensors might come at the expense of constraining the ultimate goal of context-awareness. Such expense can be mitigated by “doing more with less” i.e. delving into the opportunities of using a single sensor for multiple purposes. What is desirable is a minimal-sensor solution capable of capturing both motion and physiological signals for both robust emotion recognition and additional context estimation. Among wearable sensors, chest-worn Inertial Measurement Units (IMUs) stand out as promising candidates for comprehensive context estimation, as they are well-positioned to capture motion patterns, and physiological vibrations, being the subtle cardio-respiratory signals. This raises the question of whether a single chest-worn IMU could serve as a minimal yet effective sensor for simultaneously estimating both emotional and contextual information, offering a practical alternative to traditional multi-sensor approaches. To address the challenge of using chest-worn IMU for multiple context sensing, I investigated the potential of using Seismocardiography (SCG) and Accelerometry-Derived Respiration (ADR) to estimate emotional states. First, I explored the existing capabilities of chest-worn IMUs in capturing cardio-respiratory vibrations and motion data, which I used as a knowledge base for embedding such IMUs in context sensing. Data plays a crucial role in this problem, therefore I developed a flexible software interface, ColEmo, to facilitate the scalable collection of emotion-related data and introduced a novel dataset, EmoWear, integrating physiological and motion data for emotion recognition and context awareness. Then, I examined the feasibility of using SCG for emotion recognition, comparing its performance with traditional physiological signals and evaluating its effectiveness as a single-sensor solution. This thesis presents a detailed analysis of the potential of chest-worn IMUs for emotion and context recognition. It reports on the design and validation of a SCG-based wearable system, the development and characteristics of the EmoWear dataset, and the implementation of the ColEmo platform for emotion data collection. The research also examines machine learning techniques applied to the SCG and ADR signals, assessing their effectiveness in estimating emotional states. My findings indicate that SCG-based emotion recognition achieves performance comparable to conventional physiological counterparts, demonstrating the feasibility of using a single IMU sensor for affective computing. The publicly-available EmoWear dataset provides a valuable resource for future research, highlighting the potential of combining motion and physiological data for enhanced context awareness. Additionally, the ColEmo software interface, which I published open-source, proves to be an effective tool for multi-modal emotion data collection, facilitating further advancements in wearable emotion recognition. The results pave the way for a minimal-sensor approach for context information estimation that can significantly reduce system complexity while maintaining high performance. These findings confirm that chest-worn IMUs present an opportunity to further improve emotion recognition in addition to the motion-based context information estimation. My research highlights the need for standardized methodologies in affective computing and demonstrates the viability of motion-based physiological sensing for emotion estimation. By using a single chest-worn IMU, this work presents an alternative to multi-sensor systems, reducing power consumption, and privacy concerns while maintaining robust performance. Future research should focus on refining IMU-based emotion recognition using deep learning techniques and larger datasets to enhance classification accuracy. Further validation in real-world settings is necessary to assess the practical applicability of the proposed system. The EmoWear dataset should be explored for additional context-awareness applications, such as activity recognition and stress monitoring, to maximize its utility. The ColEmo platform offers opportunities for expansion beyond emotion recognition, including cognitive load estimation, stress monitoring, and human-computer interaction. This research moves wearable technology forward by using fewer sensors, helping make affective computing more efficient, versatile, and respectful of privacy.

AI-based emotion recognition has emerged as a critical component of affect-aware educational technologies, particularly in online, large-scale, and technology-mediated learning environments where direct observation of learners’ emotional states is limited. By leveraging facial expressions, speech, physiological signals, and interaction data, emotion-aware systems aim to support adaptive instruction, learner engagement, and instructional decision-making. Despite rapid technical progress, the integration of emotion recognition into authentic educational practice remains constrained by dataset limitations, cultural bias, weak pedagogical alignment, and unresolved ethical concerns. This article presents a critical and theory-informed review of recent advances in AI-based emotion recognition in education, with particular emphasis on deep learning architectures and multimodal fusion approaches, while highlighting persistent challenges and future research directions.Purpose:This study aims to critically examine the current state of AI-based emotion recognition in educational contexts by analyzing dominant modeling paradigms, sensing modalities, application domains, and ethical implications, in order to identify key gaps between technical development and pedagogical practice.Methods/Study design/approach:The study adopts a narrative literature review approach grounded in educational and affective computing theories. Representative and influential studies were analyzed across facial, speech, physiological, and multimodal emotion recognition systems, with emphasis on deep learning, attention-based, and transformer architectures. The review synthesizes findings related to system performance, educational applications, dataset characteristics, and ethical considerations.Result/Findings:The findings indicate that multimodal emotion recognition systems generally outperform unimodal approaches in detecting learning-relevant affective states such as engagement, frustration, and confusion. Advances in deep learning, particularly attention-based and transformer models, improve temporal modeling and robustness. However, most systems rely on non-educational datasets, demonstrate limited cultural generalizability, and prioritize recognition accuracy over pedagogical interpretability and long-term educational impact. Ethical challenges related to privacy, consent, bias, and emotional surveillance remain insufficiently addressed.Novelty/Originality/Value:This study provides an integrative and pedagogically grounded synthesis that bridges affective computing research with educational theory and governance perspectives. By reframing AI-based emotion recognition as a decision-support mechanism rather than an automated control system, the article offers a conceptual foundation for responsible, culturally inclusive, and ethically aligned deployment of emotion-aware AI in education, serving as a reference for future interdisciplinary research and policy development.

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.

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].

Abstract—An Emotion Recognition-Based Activity Recommendation System aims at providing users with adequate activity recommendations based on emotional states using the latest developments within the scope of emotion recognition technology. This project would apply speech emotion recognition techniques, specifically focusing on the most current state of-the-art methods in Triangular Region Cut-Mix augmentation for the enhancement of accuracy of emotion classification while preserving audio spectrogram information related to key emotions. Furthermore, it involves a dual learning framework integrating Emotion Recognition in Conversation and Emotional Response Generation for a richer emotional analysis. This system identifies the user’s emotions from the speech input and suggests the appropriate activities to engage the users, which is of prime interest in terms of mental wellness and betterment in the user’s experience. Some of the potential enhancements would be multimodal integration, adaptive data augmentation, and real-time detection to give more extensive and interactive recommendations. That way, there would be a highly developed user-centered solution that could help most areas, from mental support and therapy to entertainment programs individually designed based on user interest. Index Terms—Speech Emotion Recognition (SER), Triangular Region Cut-Mix, Emotion Recognition in conversation(ERC) Emotional Response Generation(ERG), Multimodal Integration Transfer Learning, Data Augmentation, Natural Language Processing (NLP) Activity Recommendation , Personalized Recommendations.

Individuals with cochlear implants (CIs) show reduced word and auditory emotion recognition abilities relative to their peers with normal hearing. Modern CI processing strategies are designed to preserve acoustic cues requisite for word recognition rather than those cues required for accessing other signal information (e.g., talker gender or emotional state). While word recognition is undoubtedly important for communication, the inaccessibility of this additional signal information in speech may lead to negative social experiences and outcomes for individuals with hearing loss. This study aimed to evaluate whether the emphasis on word recognition preservation in CI processing has unintended consequences on the perception of other talker information, such as emotional state. Twenty-four young adult listeners with normal hearing listened to sentences and either reported a target word in each sentence (word recognition task) or selected the emotion of the talker (emotion recognition task) from a list of options (Angry, Calm, Happy, and Sad). Sentences were blocked by task type (emotion recognition versus word recognition) and processing condition (unprocessed versus 8-channel noise vocoder) and presented randomly within the block at three signal-to-noise ratios (SNRs) in a background of speech-shaped noise. Confusion matrices showed the number of errors in emotion recognition by listeners. Listeners demonstrated better emotion recognition performance than word recognition performance at the same SNR. Unprocessed speech resulted in higher recognition rates than vocoded stimuli. Recognition performance (for both words and emotions) decreased with worsening SNR. Vocoding speech resulted in a greater negative impact on emotion recognition than it did for word recognition. These data confirm prior work that suggests that in background noise, emotional prosodic information in speech is easier to recognize than word information, even after simulated CI processing. However, emotion recognition may be more negatively impacted by background noise and CI processing than word recognition. Future work could explore CI processing strategies that better encode prosodic information and investigate this effect in individuals with CIs as opposed to vocoded simulation. This study emphasized the need for clinicians to consider not only word recognition but also other aspects of speech that are critical to successful social communication.

Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on speech emotion recognition has been presented considering the issues related to emotional speech corpora, different types of speech features and models used for recognition of emotions from speech. Thirty two representative speech databases are reviewed in this work from point of view of their language, number of speakers, number of emotions, and purpose of collection. The issues related to emotional speech databases used in emotional speech recognition are also briefly discussed. Literature on different features used in the task of emotion recognition from speech is presented. The importance of choosing different classification models has been discussed along with the review. The important issues to be considered for further emotion recognition research in general and in specific to the Indian context have been highlighted where ever necessary.

Speech is a powerful means of expressing thoughts, emotions, and perspectives. However, accurately determining the emotions conveyed through speech remains a challenging task. Existing manual methods for analyzing speech to recognize emotions are prone to errors, limiting our understanding and response to individuals’ emotional states. To address diverse accents, an automated system capable of real-time emotion prediction from human speech is needed. This paper introduces a speech emotion recognition (SER) system that leverages supervised learning techniques to tackle cross-accent diversity. Distinctively, the system extracts a comprehensive set of nine speech features—Zero Crossing Rate, Mel Spectrum, Pitch, Root Mean Square values, Mel Frequency Cepstral Coefficients, chroma-stft, and three spectral features (Centroid, Contrast, and Roll-off) for refined speech signal processing and recognition. Seven machine learning models are employed, encompassing Random Forest, Logistic Regression, Decision Tree, Support Vector Machines, Gaussian Naive Bayes, K-Nearest Neighbors, ensemble learning, and four individual, hybrid deep learning models including Long short-term memory (LSTM) and 1-Dimensional Convolutional Neural Network (1D-CNN) with stratified cross-validation. Audio samples from diverse English regions are combined to train the models. The performance evaluation results of conventional machine learning and deep learning models indicate that the Random Forest-based feature selection model achieves the highest accuracy of up to 76% among the conventional machine learning models. Simultaneously, the 1D-CNN model with stratified cross-validation reaches up to 99% accuracy. The proposed framework enhances the cross-accent emotion recognition accuracy up to 86.3%, 89.87%, 90.27%, and 84.96% by margins of 14.71%, 10.15%, 9.6%, and 16.52% respectively.

Emotions are ubiquitous to human life. Previous studies suggest that ninety percent of the life experience of humankind is affected by at least one emotion, and that emotional state guides their thoughts and behavior. The neurological study suggests that the design of human brain ensures that the emotional brain almost always influences the rational brain. Which means every rational decision is affected by the underlying emotional state. For decades, the influence of emotional states on human life has been overlooked by the AI researchers until recently. For the last twenty years, the study on different aspects of emotions and its detection for the more significant cause is gaining considerable attention and importance. With the advent of the Fourth Industrial Revolution (4IR), advancements in Artificial Intelligence (AI) is already touching new heights and in this context successful detection of emotional states by AI systems will be another big leap towards more realistic social AI agents. This chapter will discuss in detail the importance and recent technological advancements towards emotion recognition (ER) in the backdrop of the Fourth Industrial Revolution. ER uses different types of human physical and behavioral expressions, such as facial features, speech features, and their variants. Deep Learning (DL) has emerged as one of the prominent pillars of 4IR. ER task extensively uses DL techniques for classifying different emotions. So, this chapter includes a discussion of relevant technical aspects of DL. Finally, it includes a brief description of a newly proposed Speech Emotion Recognition (SER) model as a case study of the ER systems.

Emotional states produce physiological alterations in the vocal tract introducing variability in the acoustic parameters of speech. Emotion recognition in speech can be used in human-machine interaction applications, speaker verification, analysis of neurological disorders and psychological diagnostic tools. This paper proposes the use of Mel Frequency Cesptral Coefficients (MFCC) for automatic detection of emotions in running speech. Experiments were conducted on the Berlin emotional speech database for a three- class problem (anger, boredom and neutral emotional states). In order to evaluate the discrimination ability of the features three different classifiers were implemented: k-nearest neighbor, Bayesian Linear and quadratic. The highest accuracy results are obtained when neutral and anger emotions are evaluated.

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

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.

-- 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

Emotional speech recognition: Resources, features, and methods