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International Journal for Research in Applied Science & Engineering Technology (IJRASET)

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538
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Volume 11 Issue III Mar 2023- Available at www.ijraset.com
Table1: Comparision Table
Emotion detection Speech Input Kullback-Leibler (KL) distance For Neutral vs. Emotionalare83% and 77%
Emotion detection Speech Input And Text input.
Mel-Frequency Cepstral Coefficient (MFCC)
Faster execution time accuracy for 3 scenarios is: 73.57%, 77.67%, 82.01% and slower execution time accuracy for 3 scenarios are: 73.80%, 80.50%, and 81.65%.
Classified only neutral or Emotional but not specific emotion.
Detection is observed more on slow time execution but slow time execution takes more amount of time to detect the emotion.
Zeenat Tariq[3]
Dario Bertero[6]
Emotion detection Speech Input deep learning model (CNN).
- Proposed a speech emotion recognizer but few emotions are detected.
MFCC, MEL, and Chroma
- Detected the emotion but to less number of speeches.
Emotion detection Speech Input CNN 66.1%. False detections are made in detecting emotions and also detected one emotion i.e, sad emotion. Utkarsh Garg[5] Emotion detection Speech Input and text input
IV. ANALYSIS AND DISCUSSION
The analysis of emotion detection based on the literaturesurvey is included in this part. we have taken into account that includes accuracy based and objective based and datasetbased analysis.
V. CONCLUSION
This study includes about the speech emotion detection based on different techniques used and other emotion detection methods. We have included the parameters in emotion detection are Speech input, Database, Accuracy. Most of the speech emotion detection methods did not included the noise reduction in the speech and we included CNN with SVM for improving accuracy and performance of the model. Computational complexity ,ability of work and speed of the model are the challenges which are addressed in further.
References
[1] S. R. Kadiri and P. Alku, "Excitation Features of Speech for Speaker-Specific Emotion Detection," in IEEE Access, vol. 8,pp. 60382-60391, 2020, doi: 10.1109/ACCESS.2020.2982954.
[2] Z. Tariq, S. K. Shah and Y. Lee, "Speech Emotion Detection using IoT based Deep Learning for Health Care," 2019 IEEE International Conference on Big Data (Big Data), 2019, pp. 4191-4196, doi: 10.1109/BigData47090.2019.9005638.
[3] U. Garg, S. Agarwal, S. Gupta, R. Dutt and D. Singh, "Prediction of Emotions from the Audio Speech Signals usingMFCC, MEL and Chroma," 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), 2020, pp. 87-91, doi: 10.1109/CICN49253.2020.9242635.
[4] K. -Y. Huang, C. -H. Wu, Q. -B. Hong, M. -H. Su and Y. -H. Chen, "Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp.5866-5870, doi: 10.1109/ICASSP.2019.8682283.
[5] D. Bertero and P. Fung, "A first look into a Convolutional Neural Network for speech emotion detection," 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 5115-5119, doi: 10.1109/ICASSP.2017.7953131.