DSE-TTS: Dual Speaker Embedding for Cross-Lingual Text-to-Speech
Sen Liu Yiwei Guo Chenpeng Du Xie Chen Kai Yu
Although high-fidelity speech can be obtained for intralingual speech
synthesis, cross-lingual text-to-speech (CTTS) is still far from satisfactory
as it is difficult to accurately retain the speaker timbres(i.e. speaker
similarity) and eliminate the accents from their first language(i.e.
nativeness). In this paper, we demonstrated that vector-quantized(VQ) acoustic
feature contains less speaker information than mel-spectrogram. Based on this
finding, we propose a novel dual speaker embedding TTS (DSE-TTS) framework for
CTTS with authentic speaking style. Here, one embedding is fed to the acoustic
model to learn the linguistic speaking style, while the other one is integrated
into the vocoder to mimic the target speaker's timbre. Experiments show that by
combining both embeddings, DSE-TTS significantly outperforms the
state-of-the-art SANE-TTS in cross-lingual synthesis, especially in terms of
nativeness.