# Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis
## 2 Related Work  
Speech in various languages can be synthesized for a monoglot speaker in cross-lingual speech synthesis. In the early stages, people often use monolingual or simple multilingual TTS to synthesize cross-lingual speech, leading to switching languages accompanied by voice switching. To address this problem, [PERSON] et al. [23] built a polyglot TTS using a bilingual speech corpus recorded by a bilingual speaker. In [24], Mandarin and English context-dependent HMM states were shared, and the mapping was learned from a bilingual dataset recorded by a bilingual speaker. However, these cross-lingual TTS systems are mainly limited to constructing a multilingual corpus, characterized by a time-consuming, tedious and costly development path. Typically, only the data from monoglot speakers in different languages are available. Therefore, modern cross-lingual TTS systems are expected to generate a high-quality same-speaker voice for various languages using a monolingual speech corpus. Modern cross-lingual speech synthesis mainly faces two challenges. One challenge is that speaker-specific information and language characteristics are directly correlated, i.e., each speaker speaks only one language, making it very hard to maintain consistent voices when language switching, especially within an utterance. In [25], the authors attempted to factorize speaker-specific and language-specific information for HMM-based SPSS. The experiment showed that speaker and language factorization could disentangle speaker-specific and language-specific information. Subsequently, researchers first used the language-specific and speaker-specific components of encoders for seq2 seq-based cross-language speech synthesis [26]. Moreover, researchers used the gradient reversal layer (GRL) to further disentangle speaker-specific and language-specific information [27; 28]. Another challenge is resolving the inconsistency in the phonetic systems for different languages. One approach is to assume that the input and output of the acoustic model are language-dependent while the middle layers of the acoustic model can be language-independent and shared in the cross-lingual speech synthesis. In [29; 30], the authors proposed a multilingual deep learning-based SPSS that shared hidden layers across different languages while the input and output of acoustic models were language-dependent. For cross-lingual seq2 seq TTS, researchers used language-dependent text encoders to alleviate the mutual interference of inputs from different languages [26; 31], or used language ID to distinguish the input of different languages in the language-independent text encoder [31]. Another approach is first to convert graphemes of different languages to the uniform representations of the Unicode bytes or International Phonetic Alphabet (IPA) [32; 33]. The models then consume the Unicode bytes or IPA as the input. Meta-learning (or learning-to-learn) [34] is a new machine learning approach that has gradually emerged in recent years. As early as in [35], the authors adopted a dynamic network to predict a few parameters in the main network. Specifically, the main network and dynamic network use the same input image. The main network outputs the prediction results, while the dynamic network outputs the convolutional parameters of the main network. Similarly, [PERSON] et al. [36; 37] used dynamic networks to generate all weights for convolutional neural networks (CNNs) and LSTMs. The experimental results showed that the proposed method outperformed traditional neural network-based methods. In [38], researchers utilized a dynamic network called contextual parameter generator to generate convolutional parameters of the text encoder in the seq2 seq multilingual TTS. The experimental results showed that the proposed meta-learning-based multilingual TTS could produce natural-sounding multilingual speech using more languages and less training data than previous approaches. Tibetan has three different dialects, including the Lhasa dialect, the Kang dialect, and the Amdo dialect.