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EchoMind: An AI-driven Emotional Recognition and Personalized Recommender System

  • Unique Paper ID: 174389
  • Volume: 11
  • Issue: 10
  • PageNo: 3558-3562
  • Abstract:
  • The field of speech emotion recognition (SER) is extended to enhance the interaction between humans and computers by enabling machines to recognize and understand emotions in language. SER is used in various applications in fields like healthcare, virtual assistants, customer support, and security systems. The initial SER process depends on handcrafted properties and machine learning algorithms, which tend to perform poorly while handling variations of audio patterns and noise. That being said, the advent of deep learning transformed the construction of SER through automating feature extraction and enhancing reliability in emotional classification. Techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) show outstanding performance in detecting spatial and temporal patterns of linguistic information. In addition, the hybrid model that integrates CNN and RNN enhances the accuracy of emotional classification even more. This study introduces an AI-powered emotion recognition and personalized recommendation system that utilizes deep learning for audio analysis. The system incorporates a cutting-edge SER model integrated with Ollama, a sophisticated extension that facilitates personalized recommendations and natural, two-way communication through audio input and output. The SER model detects emotions from user speech with high precision, while Ollama uses this emotional data to provide customized recommendations and engage in context-aware dialogues. This integration creates a seamless and interactive user experience, making the system highly effective for real-time applications.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 10
  • PageNo: 3558-3562

EchoMind: An AI-driven Emotional Recognition and Personalized Recommender System

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Journal no 47859

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