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• W2921343178 abstract "Voice Conversion (VC) requires an alignment of the spectral features before learning the mapping function, due to the speaking rate variations across the source and target speakers. To address this issue, the idea of training two parallel networks with the use of speaker-independent representation was proposed. In this paper, we explore the unsupervised Gaussian Mixture Model (GMM) posteriorgram as a speaker-independent representation. However, in the GMM posteriorgram, the same phonetic information gets spread across more than one component due to the speaking style variations across the speakers. In particular, this spread is limited to a group of neighboring components for a given phone. We propose to share the posterior probability of each component with the limited number of neighboring components that are sorted based on the Kullback-Leibler (KL) divergence. We propose to employ a Deep Neural Network (DNN) and a Generative Adversarial Network (GAN)-based framework to measure the effectiveness of the proposed Inter Mixture Weighted GMM (IMW GMM) posteriorgram on the Voice Conversion Challenge (VCC) 2016 database. The relative improvement of 13.73%, and 5.25% is obtained with the proposed IMW GMM posteriorgram w.r.t. the GMM posteriorgram for the speech quality and the speaker similarity of the converted voices, respectively." @default.
• W2921343178 created "2019-03-22" @default.
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• W2921343178 date "2018-11-01" @default.
• W2921343178 modified "2023-10-14" @default.
• W2921343178 title "Novel Inter Mixture Weighted GMM Posteriorgram for DNN and GAN-based Voice Conversion" @default.
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• W2921343178 doi "https://doi.org/10.23919/apsipa.2018.8659638" @default.
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