Language-independent acoustic cloning of HTS voices. (May 2019) - Magariños, Carmen; Erro, Daniel; Banga, Eduardo R.
Highlights: We present a method to transplant speaker identity between synthetic voice models. Suitable for statistical parametric speech synthesis based on hidden Markov models. The method does not require any phonetic or linguistic information. Subjective and objective tests show a good performance of the proposed approach. Our method outperforms the Kullback–Leibler divergence-based transform mapping. Abstract: Speaker adaptation techniques can be classified as intra-lingual or cross-lingual depending on whether or not the source model and the target speaker employ the same language. Most of the work in this field has been focused on the first case, while the second one has been less explored. In this paper we address the cross-lingual paradigm in the framework of a HMM-based speech synthesis system by further developing a formerly proposed approach. This method is able to clone a given speaker into a different language by combining the linguistic structure and the acoustic characteristics of two HTS models. In this work, we discuss the extension of the adaptation procedure to some other source model parameters that were kept unmodified in the initial version, and compare the performance of both versions by means of subjective and objective tests. These results are also contrasted with those obtained by a KLD-based technique proposed in the literature for a similar purpose. While no significant preference for any of the versions of our method is observed, our approach clearly outperforms the KLD-based technique.