Kannada Speech Emotion Recognition Using Ensembling Techniques

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@article{1704436,
      author = {Smrithi Baliga, Sapna H M, Shreyas N, Yogesh Gowda V, Dr Chandrashekar M Patil; Prof. Audre Arlene},
      title = {Kannada Speech Emotion Recognition Using Ensembling Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {250-255},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704436.pdf},
      abstract = {This study explores the development of a speech emotion recognition system for the Kannada language, using a dataset of audio recordings labeled with six emotion categories: happiness, sadness, anger, fear, and neutral. We used a combination of acoustic features and machine learning algorithms, including Mel-frequency cepstral coefficients (MFCCs), to classify emotions in the audio recordings. Our results show that the proposed system achieves an average accuracy of 75% on the Kannada emotion dataset, outperforming existing baseline models. These findings suggest that Kannada speech emotion recognition can be achieved with high accuracy using a combination of acoustic features and machine learning algorithms like RNN, CNN and DBN, paving the way for further research in this area.},
      keywords = {Speech Emotion Recognition, Mel-Frequency Cepstral Coefficients, Recurrent Neural Network, Deep Belief Network},
      month = {May},
  }