Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Classification of Emotion using Different No. of MFCCs

Author : Pramod Mehra 1 Parag Jain 2

Date of Publication :22nd February 2018

Abstract: In this paper, the spectral features are separated from speech and used to perceive the feelings from speech. As Speech has been utilized as a vital method of correspondence since the time immemorial. Feelings are a basic piece of normal speech correspondence. The vast majority of the present speech frameworks can process studio recorded nonpartisan speech with more prominent precision. Hence, a need is felt to refresh speech preparing frameworks with the ability to process feelings. The part of feeling handling makes the current speech frameworks more practical and significant. In this work, spectral highlights are extricated from speech to perform feeling grouping. mel recurrence cepstral coefficients and their subsidiaries (speed and increasing speed coefficients) are investigated as highlights. Gaussian blend models are proposed as classifiers. The feelings considered in this examination are outrage, satisfaction, unbiased, pity and astonishment. The speech feeling database utilized as a part of this work is semi-common in nature, which has been gathered from the exchanges of performing artists/on-screen characters.

Reference :

    1. D. Ververidis, C. Kotropoulos, and I. Pitas, “Automatic emotional speech classification,” in ICASSP, pp. I593–I596, IEEE, 2016.
    2. Shashidhar G Koolagudi and K. Sreenivasa Rao, “Emotion Recognition from Speech: A Review”, International Journal of Speech Technology, Volume 15, Issue 2, pp 99-117, Springer, June 2012.
    3.  Van Bezooijen, “The Characterisitcs and Recognizability of Vocal Expression of Emotions”, Drodrecht,The Netherlands: Foris, 1994
    4. Hansen, J. H. L., Cairns, D. A. ICARUS: Source generator based real-time recognition of speech in noisy stressful and Lombard effect environments, Speech Communication 16, pp 391–422, 1995.
    5. D. O’Shaughnessy, Speech Communication Human and Mechine, Addison- Wesley publishing company, 1999.
    6. M. Schroder, “Emotional speech synthesis: A review,” in 7th European Conference on Speech Communication and Technology, (Aalborg, Denmark), Sept. 2001.
    7. T. L. Pao, Y. T. Chen, J. H. Yeh, and W. Y. Liao, “Combining acoustic features for improved emotion recognition in mandarin speech,” in ACII (J. Tao, T. Tan, and R. Picard, eds.), (LNCS 3784), pp. 279–285, Springer-Verlag Berlin Heidelberg, 2005.
    8. Shashidhar G. Koolagudi, Sudhamay Maity, Vuppala Anil Kumar, Saswat Chakrabarti, and K. Sreenivasa Rao, ”IITKGP-SESC: Speech Database for Emotion Analysis” in IC3, CCIS 40, pp. 485-492, Springer-Verlag, Berlin, Heidelberg 2009.
    9. D. Neiberg, K. Elenius, and K. Laskowski, “Emotion recognition in spontaneous speech using gmms,” in INTERSPEECH - ICSLP, (Pittsburgh, Pennsylvania), pp. 809–812, 17-19 September 2006.
    10. Li Y. and Zhao Y. Recognizing emotions in speech using short-term and long-term features. Proc. of the international conference on speech and language processing. pp. 2255-2258, 1998.

Recent Article