Robust emotion recognition from speech: Gamma tone features and models-Reference-Cited by-同舟云学术

Robust emotion recognition from speech: Gamma tone features and models

Published:2018-08-04 Issue:3 Volume:21 Page:723-739
ISSN:1381-2416
Container-title:International Journal of Speech Technology
language:en
Short-container-title:Int J Speech Technol

Author:

Revathi A.,Sasikaladevi N.^ORCID,Nagakrishnan R.,Jeyalakshmi C.

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Link

http://link.springer.com/article/10.1007/s10772-018-9546-1/fulltext.html

Reference28 articles.

1. Anagnostopoulos, C.-N.,·Iliou, T., & Giannoukos, I. (2015). Features and classifiers for emotion recognition from speech: A survey from 2000 to 2011. Artificial Intelligence Review, 43, 155–177.

2. Babu, M., Arun Kumar, M. N., & Santhosh, S. M. (2014). Extracting MFCC AND GTCC features for emotion recognition from audio speech signals. International Journal of Research in Computer Applications and Robotics, 2(8), 46–63.

3. Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., & Weiss, B. (2005). A database of german emotional speech (EMO-DB). Proceedings Interspeech. Lissabon, Portugal. http://emodb.bilderbar.info/start.html .

4. Garg, E., & Bahl, M. (2014). Emotion recognition in speech using gammatone cepstral coefficients. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 3(10), 285–291.

5. Kaur, I., Kumar, R., Kaur, P. (2017). Speech emotion detection based on optimistic—DNN (Deep Neural Network) approach. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 6(4), 150–156.

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