Multilayer Neural Network Based Speech Emotion Recognition for燬mart燗ssistance

Kumar, Sandeep; Haq, MohdAnul; Jain, Arpit; Jason, C. Andy; Moparthi, Nageswara Rao; Mittal, Nitin; Alzamil, Zamil S.

doi:10.32604/cmc.2023.028631

Cited by 33 publications

(6 citation statements)

References 22 publications

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“…Research such as [20] specified that this type of method will facilitate the creation of voicecontrolled systems, fulfilling the essential requirements of human beings and taking into account the possible scenarios of human-machine collaboration, achieving a significant improvement. Also, Kumar et al [35] described that the proposed algorithms offer better performance during their operation than existing technologies, suggesting the support vector machine algorithm. The authors in [36] discussed and recommended using seizure model-centered machine learning algorithms for action recognition.…”

Section: Resultsmentioning

confidence: 99%

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Peña-Cáceres,

Silva-Marchan,

Albert

et al. 2023

CMC

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The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home. It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories, subcategories, and actions from sentences. A dataset labeled with relevant information about categories, subcategories, and actions related to human actions in the smart home is used. The methodology uses machine learning techniques implemented in Python, extracting features using CountVectorizer to convert sentences into numerical representations. The results show that the classification model is able to accurately predict categories, subcategories, and actions based on sentences, with 82.99% accuracy for category, 76.19% accuracy for subcategory, and 90.28% accuracy for action. The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home, supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.

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Section: Resultsmentioning

confidence: 99%

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Peña-Cáceres,

Silva-Marchan,

Albert

et al. 2023

CMC

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“…Many of these features are categorized further based on numerous emotional factors. In particular, their study demonstrates that deep RNNs outperform conventional ML algorithms when identifying emotions in music based on instrument categories, which is a key finding in the relevant literature.The need to give piano students a way to evaluate their playing performance is highlighted, so they can get helpful feedback [24][25][26][27] . The realtime recognition of single notes and the non-real-time recognition of multiple notes are both handled by this method.…”

Section: Literature Reviewmentioning

confidence: 99%

Application and algorithm optimization of music emotion recognition in piano performance evaluation

Zhang,

Cai,

Zhang

2024

Environ. Soc. Psychol.

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In the current research, we integrate distinct learning modalities—Curriculum Learning (CL) and Reinforcement Learning (RL)—in an attempt to develop and optimize Music Emotion Recognition (MER) in piano performance. Classical approaches have never been successful when applied in the field of determining the degree of emotion in the music of the piano, owing to the substantial complexity required. Addressing this particular issue is the primary motivation for the present endeavour. In an approach that’s comparable to how human beings acquire information, it trains the RL agent CL in phases; such an approach improves the student’s learning model in understanding the diverse emotions expressed by musical compositions. A higher rating of performance can be achieved after learning the model to recognize emotions more effectively and precisely. A set of piano melodies with emotional content notes has been included in the EMOPIA repository for use when conducting the process of evaluation. In order to benchmark the proposed approach with different models, parameters including R2, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were deployed. Studies indicate that the recommended approach accurately recognizes the emotions expressed by piano-playing music. In challenging tasks like MER, the significance of implementing the CL paradigm with the RL has been emphasized using the outcomes mentioned earlier.

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“…The deep neural network is a subfield of AI and directly learns features from the data before making decisions. In numerous fields, including speech recognition [27][28][29][30], image processing [31][32][33][34], natural language processing [35,36], and bioengineering [37], deep learning algorithms have demonstrated that they are the most effective and exceptional machine learning algorithms. Additionally, several studies demonstrated that deep learning algorithms outperform traditional machine learning methods when applied to various complex learning problems [38,39].…”

Section: Deep Neural Networkmentioning

confidence: 99%

Text Augmentation-Based Model for Emotion Recognition Using Transformers

Mohammad,

Khan,

Nawaz Khan Marwat

et al. 2023

Computers, Materials &Amp; Continua

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Emotion Recognition in Conversations (ERC) is fundamental in creating emotionally intelligent machines. Graph-Based Network (GBN) models have gained popularity in detecting conversational contexts for ERC tasks. However, their limited ability to collect and acquire contextual information hinders their effectiveness. We propose a Text Augmentation-based computational model for recognizing emotions using transformers (TA-MERT) to address this. The proposed model uses the Multimodal Emotion Lines Dataset (MELD), which ensures a balanced representation for recognizing human emotions. The model used text augmentation techniques to produce more training data, improving the proposed model's accuracy. Transformer encoders train the deep neural network (DNN) model, especially Bidirectional Encoder (BE) representations that capture both forward and backward contextual information. This integration improves the accuracy and robustness of the proposed model. Furthermore, we present a method for balancing the training dataset by creating enhanced samples from the original dataset. By balancing the dataset across all emotion categories, we can lessen the adverse effects of data imbalance on the accuracy of the proposed model. Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods, achieving a weighted F1 score of 62.60% and an accuracy of 64.36%. Overall, the proposed TA-MERT model solves the GBN models' weaknesses in obtaining contextual data for ERC. TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding. The balanced dataset and the additional training samples also enhance its resilience. These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

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Multilayer Neural Network Based Speech Emotion Recognition for燬mart燗ssistance

Cited by 33 publications

References 22 publications

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Application and algorithm optimization of music emotion recognition in piano performance evaluation

Text Augmentation-Based Model for Emotion Recognition Using Transformers

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