# Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis
## 3 Baseline of Mandarin-Tibetan Cross-Lingual Speech Synthesis  
We use two kinds of Tacotron2-based Mandarin-Tibetan cross-lingual TTS as the baseline system. The two baseline systems are called shared encoder Mandarin-Tibetan cross-lingual TTS and separate encoder Mandarin-Tibetan cross-lingual TTS, respectively. The shared encoder Mandarin-Tibetan cross-lingual TTS is shown in Figure 2. To make the original Tacotron2-based TTS better adapt to cross-lingual speech synthesis for the shared encoder Mandarin-Tibetan cross-lingual TTS, we change the original Tacotron2-based TTS as follows. 1. The residual connection and batch normalization layer are added to the text encoder of the original Tacotron2, named the residual text encoder. The output of the residual text encoder is shown in Equation (1). \[\text{Encoder}_{\text{res}}\Big{(}\left\{\boldsymbol{x}_{j}\right\}_{j=1}^{N }\Big{)}=\text{Encoder}\Big{(}\left\{\boldsymbol{x}_{j}\right\}_{j=1}^{N} \Big{)}+\text{ Phone}\_\text{embedding}\left(\left\{\boldsymbol{x}_{j} \right\}_{j=1}^{N}\right)\] (1) where \(\left\{\boldsymbol{x}_{j}\right\}_{j=1}^{N}\) represents the input phoneme sequences, \(\text{Encoder}\Big{(}\left\{\boldsymbol{x}_{j}\right\}_{j=1}^{N}\Big{)}\) represents the encoder output of the original Tacotron2-based TTS, and \(\text{Phone}\_\text{embedding}\left(\left\{\boldsymbol{x}_{j}\right\}_{j=1 }^{N}\right)\) represents the embedding of the input phoneme sequences. 2. In the Mandarin-Tibetan cross-lingual speech synthesis task, language embedding and speaker embedding are added to the Tacotron2-based TTS to distinguish the pronunciation characteristics of different speakers in different languages. In particular, the speaker embedding and the output of attention LSTM are concatenated as the input of the decoder LSTM to improve the similarity between the synthesized speech and the original speech. Figure 1: The typical longest Tibetan character. The spelling order of a Tibetan character is prescript, superscript, radical, subscript, vowel, postscript, and post-postscript. A Tibetan character has at least one radical. 3. We use an adversarial speaker classifier to disentangle the speaker-specific information from the text encoder. The adversarial speaker classifier consists of a gradient reversal layer, two fully connected layers, and a softmax layer. Due to the introduction of the adversarial speaker classifier, the new training loss function is shown in Equation (2). \[\text{LOSS}_{\text{Total}}\,=\,\text{LOSS}_{DEC}+\text{LOSS}_{\text{Speaker\_ classifier}}\] (2) where \(\text{LOSS}_{DEC}\) represents the loss between the synthesized speech and the target speech, \(\text{LOSS}_{\text{Speaker\_ classifier}}\,\) represents the loss of speaker classification predicted by the adversarial speaker classifier. 4. Chinese and Tibetan characters are different from English and other Roman characters-based languages. Because there is no distinct separator between adjacent words, an occasional word segmentation error leads to semantic confusion and prosodic errors in speech synthesis. In [44], the experiments show that enhanced input text by  
Figure 2: The shared encoder Mandarin-Tibetan cross-lingual TTS. The shared residual encoder handles phoneme sequences from different languages with explicit language embedding. In addition, a prosody generator is designed to extract the prosodic information from a sentence. The prosody generator includes a text analyzer, a feature vector extraction module, a question set, and a hidden feature extraction module. In the figure, the input is a Tibetan sentence (left) or a Chinese sentence (right), which means that all houses have been built in the last ten years. integrating prosodic information (e.g., prosodic word boundaries, prosodic phrase boundaries, HTS-based context-dependent information) significantly improves the naturalness of the synthesized speech in seq2 seq TTS.