Vowels and Prosody Contribution in Neural Network Based Voice Conversion Algorithm with Noisy Training Data

Olaide Ayodeji Agbolade
Additional contact information
Olaide Ayodeji Agbolade: Federal University of Technology, Akure, Nigeria

European Journal of Engineering and Technology Research, 2020, vol. 5, issue 3, 229-233

Abstract: This research presents a neural network based voice conversion model. While it is a known fact that voiced sounds and prosody are the most important component of the voice conversion framework, what is not known is their objective contributions particularly in a noisy and uncontrolled environment. This model uses a 3 layer feedforward neural network to map the Linear prediction analysis coefficients of a source speaker to the acoustic vector space of the target speaker with a view to objectively determine the contributions of the voiced, unvoiced and supra-segmental components of sounds to the voice conversion model. Results showed that vowels “a”, “i”, “o” have the most significant contribution in the conversion success. The voiceless sounds were also found to be most affected by the noisy training data. An average noise level of 40 dB above the noise floor were found to degrade the voice conversion success by 55.14 percent relative to the voiced sounds. The result also show that for cross-gender voice conversion, prosody conversion is more significant in scenarios where a female is the target speaker.

Keywords: voice conversion; speech morphing; formant; voiced and unvoiced speech; prosody; suprasegmental (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/ejeng/article/view/61802 Abstract page (text/html)
https://eu-opensci.org/index.php/ejeng/article/download/61802/12372 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:5:y:2020:i:3:id:61802

DOI: 10.24018/ejeng.2020.5.3.1802

Access Statistics for this article

More articles in European Journal of Engineering and Technology Research from European Open Science
Bibliographic data for series maintained by Support ().