Efficient model re-estimation in voice conversion

Voice conversion systems aim at converting an utterance spoken by one speaker to sound as speech uttered by a second speaker. Over the last few years, the interest towards voice conversion has risen immensely. Gaussian mixture model (GMM) based techniques have been found to be efficient in the transformation of features represented as scalars or vectors. However, reasonably large amount of aligned training data is needed to achieve good results. To solve this problem, this paper presents an efficient model re-estimation scheme. The proposed technique is based on adjusting an existing well-trained conversion model for a new target speaker with only a very small amount of training data. The experimental results provided in the paper demonstrate the efficiency of the re-estimation approach in line spectral frequency conversion and show that the proposed approach can reach good performance while using only a very limited amount of adaptation data.