Probabilistic Voice Conversion Using Gaussian Mixture Models
نویسنده
چکیده
This paper explores the topic of voice conversion as explored in a joint project with Percy Liang (EECS, Berkeley). For our purposes, voice conversion is the process of modifying the speech signal of one speaker (source) so that it sounds as thought it had been pronounced by a different speaker (target). By using a Gaussian mixture model (GMM) to model the features of the source speaker, we can learn a mapping from the source to the target. We explore the effect of increasing the complexity of our model on the result of the conversion. Further discussion includes ideas to extend the GMM framework into a potential HMM as well as ways to improve the features that are used in our system.
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