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Common voice conversion methods are based on Gaussian Mixture Modeling (GMM), which requires exhaustive training (typically lasting hours), often leading to ill-conditioning if the dataset used is too small. We propose a new conversion method that is trained in seconds, using either small or large scale datasets. The proposed Grid-Based (GB) method is based on sequential Bayesian tracking, by which the conversion process is expressed as a sequential estimation problem of tracking the target spectrum based on the observed source spectrum. The converted MFCC vectors are sequentially evaluated using a weighted sum of the target training set used as grid-points. To improve the perceived quality of the synthesized signals, we use a post-processing block for enhancing the global variance. Objective and subjective evaluations show that the enhanced-GB method is comparable to classic GMM-based methods, in terms of quality, and comparable to their enhanced versions, in terms of individuality.

Metric learning includes a wide range of algorithms aiming to improve the classification accuracy by capturing the spatial structure of the training set. The performance of those (supervised) methods greatly depends on the amount of labeled data available for training. In practice, however, it is usually not easy to obtain a large-scale labeled set, as opposed to an unlabeled one. In this paper we propose a new method for metric learning using a small-scale labeled set and a large-scale unlabeled set. This method can be applied in two setups — a Semi-Supervised (SS) classification setup and a Domain Adaptation (DA) setup. We used two sources of hand-written digits images to demonstrate the performance of our proposed method. We show that in both SS and DA setups, the proposed method leads to fewer classification errors compared to Euclidean distance and to Large Margin Nearest Neighbor (LMNN).