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This paper proposes a voice morphing system for people suffering fromLaryngectomy, which is the surgical removal of all or part of the larynx or thevoice box, particularly performed in cases of laryngeal cancer. A primitivemethod of achieving voice morphing is by extracting the source's vocalcoefficients and then converting them into the target speaker's vocalparameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mappingthe coefficients from source to destination. However, the use of thetraditional/conventional GMM-based mapping approach results in the problem ofover-smoothening of the converted voice. Thus, we hereby propose a uniquemethod to perform efficient voice morphing and conversion based on GMM,whichovercomes the traditional-method effects of over-smoothening. It uses atechnique of glottal waveform separation and prediction of excitations andhence the result shows that not only over-smoothening is eliminated but alsothe transformed vocal tract parameters match with the target. Moreover, thesynthesized speech thus obtained is found to be of a sufficiently high quality.Thus, voice morphing based on a unique GMM approach has been proposed and alsocritically evaluated based on various subjective and objective evaluationparameters. Further, an application of voice morphing for Laryngectomees whichdeploys this unique approach has been recommended by this paper.

Analysis of a Modern Voice Morphing Approach using Gaussian Mixture Models for Laryngectomees