Comparing GMM-based speech transformation systems

Mesbahi, Larbi; Barreaud, Vincent; Boëffard, Olivier

doi:10.21437/interspeech.2007-556

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to achieve an effective personality change it is needed to change the glottal excitation characteristics of the source speaker to match exactly as that of the target, so a prediction rule has been trained on the aspects of the excitation signals of the target speaker. In the transforming stage, the source vocal tract features were extracted and modified based on the conversion rule from the training stage, meanwhile, converted excitation signals were obtained by predicting from the transformed vocal tract features based on the prediction rule [15] [16]. Finally, a continuous waveform was obtained in the LPC synthesis model by synthesizing all these parameters.…”

Section: Fig 2: Speech Production Modelmentioning

confidence: 99%

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

Chadha¹,

Savardekar²,

Padhya³

2012

IJCA

View full text Add to dashboard Cite

This paper proposes a voicemorphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A primitive method of achieving voice morphing is by extracting the source's vocal coefficients and then converting them into the target speaker's vocal parameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mapping the coefficients from source to destination. However, the use of the traditional/conventional GMM-based mapping approach results in the problem of over-smoothening of the converted voice. Thus, we hereby propose a unique method to perform efficient voice morphing and conversion based on GMM, which overcomes the traditional-method effects of over-smoothening. It uses a technique of glottal waveform separation and prediction of excitations and hence the result shows that not only oversmoothening is eliminated but also the transformed vocal tract parameters match with the target. Moreover, the synthesized 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 also critically evaluated based on various subjective and objective evaluation parameters. Further, an application of voice morphing for Laryngectomees which deploys this unique approach has been recommended by this paper.

show abstract

Section: Fig 2: Speech Production Modelmentioning

confidence: 99%