  In this paper, we study the disentanglement of speaker and language
representations in non-autoregressive cross-lingual TTS models from various
aspects. We propose a phoneme length regulator that solves the length mismatch
problem between IPA input sequence and monolingual alignment results. Using the
phoneme length regulator, we present a FastPitch-based cross-lingual model with
IPA symbols as input representations. Our experiments show that
language-independent input representations (e.g. IPA symbols), an increasing
number of training speakers, and explicit modeling of speech variance
information all encourage non-autoregressive cross-lingual TTS model to
disentangle speaker and language representations. The subjective evaluation
shows that our proposed model can achieve decent naturalness and speaker
similarity in cross-language voice cloning.
