An approach to voice conversion based on non-linear canonical correlation analysis

Authors:
Zhihua Jian
Affiliations:
School of Communication Engineering, HangZhou DianZi University, HangZhou, China
Venue:
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Year:
2009

Citing 8
Cited 0

Transformation of formants for voice conversion using artificial neural networks

Speech Communication - Special issue: voice conversion: state of the art and perspectives
On the use of spectral transformation for speaker adaptation in HMM based isolated-word speech recognition

Speech Communication
Speaker transformation algorithm using segmental codebooks (STASC)

Speech Communication
Nonlinear canonical correlation analysis by neural networks

Neural Networks
High-resolution voice transformation

High-resolution voice transformation
Canonical correlation analysis based on information theory

Journal of Multivariate Analysis
Statistical Approach for Voice Personality Transformation

IEEE Transactions on Audio, Speech, and Language Processing
Quality-enhanced voice morphing using maximum likelihood transformations

IEEE Transactions on Audio, Speech, and Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality. The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using non-linear canonical correlation analysis (NLCCA) based on jointed Gaussian mixture model. Speaker individuality transformation was achieved mainly by altering vocal tract characteristics represented by line spectral frequencies (LSF). To obtain the transformed speech sounded more like the target voices, prosody modification is involved through residual prediction. Both objective and subjective evaluations were conducted. The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the minimum mean square error (MMSE) estimation.