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Neutral Speech to Target Speech Conversion by Prosodic Modification

Published on July 2018 by Shreegowri A J, D. J. Ravi
journal_cover_thumbnail

National Conference on Electronics, Signals and Communication
Foundation of Computer Science USA
NCESC2017 - Number 1
July 2018
Authors: Shreegowri A J, D. J. Ravi
cb35d4e4-6caa-4e98-991c-4858d884c638

Shreegowri A J, D. J. Ravi . Neutral Speech to Target Speech Conversion by Prosodic Modification. National Conference on Electronics, Signals and Communication. NCESC2017, 1 (July 2018), 4-6.

@article{
author = { Shreegowri A J, D. J. Ravi },
title = { Neutral Speech to Target Speech Conversion by Prosodic Modification },
journal = { National Conference on Electronics, Signals and Communication },
issue_date = { July 2018 },
volume = { NCESC2017 },
number = { 1 },
month = { July },
year = { 2018 },
issn = 0975-8887,
pages = { 4-6 },
numpages = 3,
url = { /proceedings/ncesc2017/number1/29602-7009/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Electronics, Signals and Communication
%A Shreegowri A J
%A D. J. Ravi
%T Neutral Speech to Target Speech Conversion by Prosodic Modification
%J National Conference on Electronics, Signals and Communication
%@ 0975-8887
%V NCESC2017
%N 1
%P 4-6
%D 2018
%I International Journal of Computer Applications
Abstract

The dynamics of prosodic features are utilized for speech emotion conversion. In particular, emotion conversion of neutral speech to sad, fear, anger and happy speech is accomplished. The prosodic features considered for the study are pitch contour and duration. Subjective listening test results show that the effectiveness of perception of emotion is better in the case of pitch contour and duration for the whole utterance. The results show that the converted sad, fear, angry speech are perceived very close to natural sad, fear, anger and happy emotions.

References
  1. Amrita, Bageshree Pathak "Emotion Conversion Of Speech Signal Using Neural Network"; IJEEDC-2017
  2. "GMM-Based Emotional Voice Conversion Using Spectrum and Prosody Features"; American Journal of Signal Processing;p-ISSN: 2012.
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  5. Rao, K. S. , Yegnanarayana, B. : Prosody modi?cation using instants of signi?cant ex- citation. IEEE Transactions on Audio, Speech and Language Processing 14, 972–980 (2006)
  6. Paeschke, A. , Sendlmeier, W. F. : Prosodic characteristics of emotional speech: measurements of fundamental frequency movements. In: Speech Emotion, pp. (2000)
  7. Koolagudi, S. G. , Maity, S. , Kumar, V. A. , Chakrabarti, S. , Sreenivasa Rao, K. : IITKGP-SESC: Speech database for emotion analysis. In: Ranka, S. , et al. (eds. ) IC3 2009. CCIS, vol. 40, pp. 485–492. Springer, Heidelberg (2009)
  8. Yegnanarayana, B. , Murty, K. S. R. : Event-based instantaneous fundamental fre- quency estimation from speech signals. IEEE Transactions on Audio, Speech and Language Process 17(4), 614–625 (2009)
Index Terms

Computer Science
Information Sciences

Keywords

Prosody (the Study Of Rhythm Intonation Stress And Related Attributes In Speech)