Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks

Shashidhar G. Koolagudi; B. Kavya Vishwanath; M. Akshatha; Yarlagadda V.S. Murthy

doi:10.1007/978-981-10-1678-3_27

Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks

Shashidhar G. Koolagudi^*, B. Kavya Vishwanath, M. Akshatha, Yarlagadda V.S. Murthy

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

Voice Conversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source–target relationship from a number of utterances from source and the target. There are many applications which may benefit from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this paper, analysis on the performance of ANN-based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coefficients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features.

Original language	English
Title of host publication	Proceedings of the International Conference on Data Engineering and Communication Technology
Editors	Vikrant Bhateja, Suresh Chandra Satapathy, Amit Joshi
Publisher	Springer Verlag
Pages	275-280
Number of pages	6
ISBN (Print)	9789811016776
DOIs	https://doi.org/10.1007/978-981-10-1678-3_27
Publication status	Published - 2017
Event	1st International Conference on Data Engineering and Communication Technology, ICDECT 2016 - Lavasa City, Pune, India Duration: 10-03-2016 → 11-03-2016

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	469
ISSN (Print)	2194-5357

Conference

Conference	1st International Conference on Data Engineering and Communication Technology, ICDECT 2016
Country/Territory	India
City	Lavasa City, Pune
Period	10-03-16 → 11-03-16

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-981-10-1678-3_27

Cite this

Koolagudi, S. G., Vishwanath, B. K., Akshatha, M., & Murthy, Y. V. S. (2017). Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks. In V. Bhateja, S. C. Satapathy, & A. Joshi (Eds.), Proceedings of the International Conference on Data Engineering and Communication Technology (pp. 275-280). (Advances in Intelligent Systems and Computing; Vol. 469). Springer Verlag. https://doi.org/10.1007/978-981-10-1678-3_27

Koolagudi, Shashidhar G. ; Vishwanath, B. Kavya ; Akshatha, M. et al. / Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks. Proceedings of the International Conference on Data Engineering and Communication Technology. editor / Vikrant Bhateja ; Suresh Chandra Satapathy ; Amit Joshi. Springer Verlag, 2017. pp. 275-280 (Advances in Intelligent Systems and Computing).

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title = "Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks",

abstract = "Voice Conversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source–target relationship from a number of utterances from source and the target. There are many applications which may benefit from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this paper, analysis on the performance of ANN-based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coefficients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features.",

author = "Koolagudi, \{Shashidhar G.\} and Vishwanath, \{B. Kavya\} and M. Akshatha and Murthy, \{Yarlagadda V.S.\}",

note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2017.; 1st International Conference on Data Engineering and Communication Technology, ICDECT 2016 ; Conference date: 10-03-2016 Through 11-03-2016",

year = "2017",

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language = "English",

isbn = "9789811016776",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

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Koolagudi, SG, Vishwanath, BK, Akshatha, M & Murthy, YVS 2017, Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks. in V Bhateja, SC Satapathy & A Joshi (eds), Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol. 469, Springer Verlag, pp. 275-280, 1st International Conference on Data Engineering and Communication Technology, ICDECT 2016, Lavasa City, Pune, India, 10-03-16. https://doi.org/10.1007/978-981-10-1678-3_27

Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks. / Koolagudi, Shashidhar G.; Vishwanath, B. Kavya; Akshatha, M. et al.
Proceedings of the International Conference on Data Engineering and Communication Technology. ed. / Vikrant Bhateja; Suresh Chandra Satapathy; Amit Joshi. Springer Verlag, 2017. p. 275-280 (Advances in Intelligent Systems and Computing; Vol. 469).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks

AU - Koolagudi, Shashidhar G.

AU - Vishwanath, B. Kavya

AU - Akshatha, M.

AU - Murthy, Yarlagadda V.S.

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N2 - Voice Conversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source–target relationship from a number of utterances from source and the target. There are many applications which may benefit from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this paper, analysis on the performance of ANN-based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coefficients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features.

AB - Voice Conversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source–target relationship from a number of utterances from source and the target. There are many applications which may benefit from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this paper, analysis on the performance of ANN-based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coefficients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features.

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Koolagudi SG, Vishwanath BK, Akshatha M, Murthy YVS. Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks. In Bhateja V, Satapathy SC, Joshi A, editors, Proceedings of the International Conference on Data Engineering and Communication Technology. Springer Verlag. 2017. p. 275-280. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-10-1678-3_27

Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks

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