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Hoseini, Seyed Mehdi. (1399). Persian Speech Emotion Recognition Approach based on Multilayer Perceptron. سامانه مدیریت نشریات علمی, 2(3), 177-187. doi: 10.22054/dcm.2021.13682
Seyed Mehdi Hoseini. "Persian Speech Emotion Recognition Approach based on Multilayer Perceptron". سامانه مدیریت نشریات علمی, 2, 3, 1399, 177-187. doi: 10.22054/dcm.2021.13682
Hoseini, Seyed Mehdi. (1399). 'Persian Speech Emotion Recognition Approach based on Multilayer Perceptron', سامانه مدیریت نشریات علمی, 2(3), pp. 177-187. doi: 10.22054/dcm.2021.13682
Hoseini, Seyed Mehdi. Persian Speech Emotion Recognition Approach based on Multilayer Perceptron. سامانه مدیریت نشریات علمی, 1399; 2(3): 177-187. doi: 10.22054/dcm.2021.13682

Persian Speech Emotion Recognition Approach based on Multilayer Perceptron

International Journal of Digital Content Management
مقاله 10، دوره 2، شماره 3، خرداد 2021، صفحه 177-187    اصل مقاله (465.93 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22054/dcm.2021.13682
نویسنده
Seyed Mehdi Hoseini*
Department of Computer Science, University of Mazandaran, Babolsar, Iran.
چکیده
Emotion recognition from speech has noticeable applications within the speech-processing systems. The goal of this paper is to permit a totally natural interaction among human and system. In this paper, an attempt is made to design and implement a system to determine and detect emotions of anger and happiness in the Persian speech signals. Research on recognizing some emotions has been done in most languages, but due to the difficulty of creating a speech database, so far little research has been done to identify emotions in Persian speech. In this article, because of the dearth of a suitable database in Persian to detect feelings, before everything, a database for moods of happiness and anger and neutral (with no emotion) in Persian, including 720 sentences was set up. Then the frequency features of speech signals obtained from Fourier transform such as maximum, minimum, median and mean as well as LPC coefficients were extracted. Then, the MLP neural network was used to detect emotions of happiness and anger. Results show that our algorithm performs 87.74% accurately.
کلیدواژه‌ها
Emotion Recognition؛ Speech Processing؛ LPC Coefficients؛ Neural Network
مراجع
Garvian, D., & Ahadi, S. M. (2008). Recognition of emotional speech and identification of speech in Persian. ModaresTechnical and Engineering Journal. Electrical Engineering Special Issue. 34. [in Persian]

Mosavian, E., Norasteh, R. & Rahati, S. (2013). Recognition of human emotions using neural-fuzzy network. Proceedings of the 8th Intelligent Systems Conference. Ferdowsi University of Mashhad, Mashhad, Iran. [in Persian]

Mosavian, E., Norasteh, R., & Rahati, S. (2008). Recognition of emotions in Persian speech using fractal dimension. Proceedings of the 17th Iranian Conference on Electrical Engineering. Iran University of Science and Technology, Iran, 8, 342-348. [in Persian]

Ranganath, R., Jurafsky, D. & McFarland, D. A. (2013). Detecting friendly, flirtatious, awkward, and assertive  speech in speed-dates. Computer Speech & Language. 27, 89-115.

Arias, J. P., Busso, C., & Yoma, N. B. (2014). Shape-based modeling of the fundamental frequency contour for emotion detection in speech. Computer Speech & Language. 28, 278-294.

Hamidi, M., & Mansoorizade, M. (2012). Emotion recognition from Persian speech with neural network. International Journal of Artificial Intelligence & Applications. 3, 107-112.

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.

Bijankhan, M., Sheikhzadegan, J., & Roohani, M. R. (1994). The speech database of Farsi spoken language. Proceedings of the Australian Conference on Speech Science and Technology, 2, 826-830.

Staroniewicz, P. (2011). Automatic recognition of emotional state in Polish speech. In Toward Autonomous, Adaptive, and Context-Aware Multimodal Interfaces. Theoretical and Practical Issues, Springer, Berlin, Heidelberg, 347-353.

Staroniewicz, P. (2009). Recognition of emotional state in Polish speech-comparison between human and automatic efficiency. In European Workshop on Biometrics and Identity Management, Springer, Berlin, Heidelberg, 33-40.

Ayadi, M. E., Kamel, M. S., & Karray, F. (2011). Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern recognition. 44, 572-587.

Parvinnia, E., & Pourvahid, M. (2017). Feature extraction from speech signals to identify the feeling Persian. Proceedings of the National Conference on Computer Engineering and Information Technology. Islamic Azad University, Sepidan Branch, Iran. [in Persian]

Lopatovska, I., & Arapakis, I. (2011). Theories, methods and current research on emotions in library and information science, information retrieval and human–computer interaction. Information Processing & Management. 47, 575-592.

Addeh, A., & Maghsoudi, B. M. (2016). Control chart patterns detection using COA based trained MLP neural network and shape features. Computational Research Progress in Applied Science & Engineering. 2, 5-8.

Golilarz, N. A., & Demirel, H. (2017). Thresholding neural network (TNN) based noise reduction with a new improved thresholding function. Computational Research Progress in Applied Science & Engineering. 3.

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