# Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis
## 1 Introduction  
Speech synthesis (or text-to-speech, TTS) has been widely used in applications such as smart homes, navigation, and audiobooks. However, there is still a challenge for a computer to generate natural and expressive human-like speech.  
Generally speaking, there are several approaches for generating speech: (a) formant synthesis [1], (b) unit selection-based speech synthesis [2], (c) hidden Markov model (HMM)-based statistical parametric speech synthesis (SPSS) [3], and (d) deep learning-based speech synthesis [4; 5; 6; 7]. Unit selection-based speech synthesis generates the most natural-sounding speech by concatenating small units of prerecorded waveforms with a unit selection algorithm, but its flexibility is limited. HMM-based SPSS has been the most popular TTS technology since the 1990s. The main advantage of HMM-based SPSS over unit selection-based speech synthesis is its flexibility in changing speech features, speaking styles, and emotions. This flexibility is mainly attributed to applying many techniques for controlling variation in speech, such as adaptation [8], interpolation [9], and eigen-voice. However, the synthesized speech of HMM-based SPSS is muffled compared with natural speech [10].  
Since 2006, SPSS TTS has improved with the development of deep learning technologies. These deep learning-based statistical parametric acoustic models can be roughly divided into two categories: one adopt restricted Boltzmann machines (RBMs) [11] or deep belief networks (DBNs) [12] to improve the probability density function of HMM states, while the other is to use feed-forward neural networks (FNNs), mixture density networks (MDNs) [13], or long short term memory networks (LSTMs) [14] to model the relationship between linguistic features and acoustic parameters. Because deep learning technologies can represent the mapping between the context linguistic features and acoustic parameters well compared with statistical models commonly used in SPSS (e.g., decision trees, HMM), they help overcome the loss of detailed characteristics in synthesized speech.  
Sequence-to-sequence (seq2 seq) neural networks have been successfully applied to various tasks such as machine translation [15; 16] and speech recognition [17; 18]. In [19], [PERSON] et al. proposed a seq2 seq acoustic model for speech synthesis named Char2 Wav, consisting of a reader and a neural vocoder. The reader is an encoder-decoder model with attention. The encoder is a bidirectional recurrent neural network that encodes the input text into hidden feature representations. The decoder with attention takes the hidden feature representations of the encoder to predict intermediate acoustic representations for the neural vocoder. The neural vocoder refers to a conditional extension of SampleRNN [20] that generates raw waveform samples from intermediate representations. Subsequently, the seq2 seq TTS represented by Tacotron can be directly trained on the pairs of _<text_, _speech_> and automatically learn the alignment and mapping of the characters to spectrogram frames, which are then converted into waveforms by the neural vocoder [21]. [PERSON] et al. [22] further proposed an attention mechanisms-based transformer for replacing complex recurrent or convolutional neural networks in encoder and decoder. Recent studies show that transformer-based TTS [6; 7] is superior to Tacotron-based TTS in inference speed and robustness. Therefore, the synthesized speech quality of state-of-the-art seq2 seq TTS is close to humans' natural voices.  
The rest of the paper is organized as follows. We first introduce related work in Section 2. Then, we illustrate the baseline in Section 3 and present our meta-learning-based Mandarin-Tibetan cross-lingual speech synthesis framework in Section 4. Subsequently, we explain the experimental data and setup in Section 5. The experimental results and analysis are presented in Section 6. Finally, a brief conclusion and future work are provided in Section 7.