Emotion Detection in Speech
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In recent years, the research on emotion detection in speech has gained significant attention due to its potential applications in various fields such as healthcare, customer service, and social robotics.
This paper literature review provides a detailed and thorough overview of the current techniques and methodologies including the acoustic features-based, deep learning-based, linguistic features-based, multimodal, ensemble, and transfer learning-based approaches.
Furthermore, this paper discusses the strengths and limitations of each approach. For instance, acoustic features-based approaches are straightforward yet efficient, while deep learning-based approaches can learn complex speech signal representations.
Additionally, linguistic features-based approaches prove useful when the emotional content is closely linked to the speech content. Multimodal approaches integrate information from multiple modalities to enhance accuracy, while ensemble approaches merge multiple classifiers to improve the system’s robustness. Also, transfer learning-based approaches transfer knowledge from related tasks to improve performance in situations where there is limited training data.
The review emphasizes the importance of developing accurate and robust emotion detection systems, which will play a vital role in enhancing human-machine interaction and the success of various applications.
This paper discusses a Machine learning algorithm to discern the emotion associated with human speech developed to account for three emotions namely, normal, angry, and panicked
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