Evaluation of eigenvoice conversion based on Gaussian mixture model

Abstract

Eigenvoice conversion (EVC) has been proposed as a new framework of voice conversion (VC) based on the Gaussian mixture model (GMM) [Toda et al., ‘‘Eigenvoice Conversion Based on Gaussian Mixture Model,’’ ICSLP, Pittsburgh, Sept. 2006]. This paper evaluates the performance of EVC in conversion from one source speaker’s voice to an arbitrary target speakers’ voices. This framework trains canonical GMM (EV‐GMM) in advance using multiple parallel data sets consisting of utterance pairs of the source and many prestored target speakers. This model is adapted to a specific target speaker by estimating a small number of free parameters using a few utterances of the target speaker. This paper compares spectral distortion between converted and target voices in EVC with conventional VC based on GMM when varying the amount of training data and the number of mixtures. Results show EVC outperforms conventional VC when using small amounts of training data. EVC can effectively train a complex conversion model using the information of many prestored speakers. By contrast, conventional VC needs a large‐sized parallel data set for training. It also shows the results of subjective evaluations of speech quality and conversion accuracy for speaker individuality.