  This paper proposes a multilingual speech synthesis method which combines
unsupervised phonetic representations (UPR) and supervised phonetic
representations (SPR) to avoid reliance on the pronunciation dictionaries of
target languages. In this method, a pretrained wav2vec 2.0 model is adopted to
extract UPRs and a language-independent automatic speech recognition (LI-ASR)
model is built with a connectionist temporal classification (CTC) loss to
extract segment-level SPRs from the audio data of target languages. Then, an
acoustic model is designed, which first predicts UPRs and SPRs from texts
separately and then combines the predicted UPRs and SPRs to generate
mel-spectrograms. The results of our experiments on six languages show that the
proposed method outperformed the methods that directly predicted
mel-spectrograms from character or phoneme sequences and the ablated models
that utilized only UPRs or SPRs.
