# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 1 Introduction  
Speech synthesis is a technology that converts text into speech signals by analyzing textual features and mapping them onto acoustic representations. It utilizes digital signal processing methods to produce audible speech, enabling the transformation of text into human-like speech that users can comprehend through auditory means. Within artificial intelligence, speech synthesis integrates speech processing techniques with natural language processing and automation, driving advancements in human-machine interaction [1]. Advancements in technology and improvements in computer performance have made human-machine speech interaction a pivotal research focus in artificial intelligence. Speech synthesis, as a core technology, has garnered unprecedented attention in recent years [2]. Current research extensively explores deep learning techniques to generate realistic and fluent human-like voices, ranging from convolutional neural networks [3] and recurrent neural networks [4] to generative adversarial networks [5]. These advancements include the application of reinforcement learning, enabling deep learning models to adapt voice synthesis based on contextual cues. Contemporary speech synthesis has evolved from traditional waveform, parameter, and rule-based methods to those based on deep learning. Since the early 21 st century, deep learning technologies, particularly neural networks applied in speech synthesis, have rapidly advanced. Google's WaveNet model [6], introduced in 2016, marked a significant milestone in deep learning-based speech synthesistechnology. WaveNet directly generates speech waveforms using deep convolutional neural networks, significantly enhancing the naturalness and quality of synthesized speech. Subsequent models like Tacotron [7] and Tacotron2 [8] further propelled text-to-speech conversion towards end-to-end models, achieving more efficient and natural speech synthesis. Tacotron has also been utilized in voice cloning, multilingual synthesis, and emotional speech synthesis [2; 9]. Current deep learning approaches such as Transformer TTS [10], FastSpeech [11], and FastSpeech2 [12] not only enhance synthesis efficiency and speech quality but also reduce computational resource demands, enabling broader practical applications. In the field of Tibetan speech synthesis, technological development has transitioned from traditional methods to modern end-to-end approaches. Initial Tibetan speech synthesis systems heavily relied on HMM statistical parameter models [13], demonstrating effectiveness with limited data but facing challenges in naturalness and audio quality. With the introduction of deep learning technologies, Tibetan speech synthesis has gradually shifted towards end-to-end neural network models. Initially, a Tibetan speech synthesis model was developed based on the Tacotron framework [14], utilizing the classic Griffin-Lim vocoder [15] to generate speech waveforms, which generated a smooth waveform and led to a poor quality of synthesized speech. To address this issue, Tacotron2 emerged and was applied to speech synthesis in Tibetan Lhasa [16]. Tacotron2 employs the WaveNet vocoder to enhance the quality of synthesis; nevertheless, its reliance on the LSTM [17] model for encoding contextual information presents challenges in constructing models with long-term dependencies. Conversely, Transformer TTS [18] relies entirely on the self-attention mechanism and can effectively manage long-term dependencies by constructing context vectors from various perspectives using the multi-head attention mechanism. However, Transformer TTS is hindered by its inability to parallelize the inference process and the issue of misaligned attention codecs. In response, the FastSpeech2 method has been applied for Tibetan speech synthesis [19]. Since the FastSpeech2 model utilizes a non-autoregressive approach, it boasts a faster generation speed than traditional autoregressive models.