# Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet
## 1 Introduction  
The main goal of speech synthesis technology is to convert text information into sounds in order to use various voice applications and improve the user's experience. This makes human life more exciting and creates many conveniences in people's lives. At this stage, speech synthesis technology (text to speech, TTS) has become an integral part of the field of human-computer interaction applications, has attracted the attention of various application fields in the scientific community [1], and has achieved remarkable results. In the past few decades, speech synthesis models have been developing and evolving. Among them, speech splicing technology, acoustic parameter, and statistical speech synthesis models are the most common. However, the construction and implementation of these models are very complex, require extensive linguistic knowledge, and require a large investment of time and effort. Compared with human speech, the rhythm and clarity of synthesized audio are often unstable and sound very unnatural. [PERSON] et al. proposeddeep learning (DL), a new machine learning architecture in 2006 [2], which effectively combines deep neural networks and unsupervised layer-by-layer initialization technology, thus greatly improving the efficiency and accuracy of a new generation of artificial intelligence. In recent years, deep learning has become a key research project of the new generation of artificial intelligence. Models based on deep learning have been widely used in related fields, such as visual processing and natural language processing, and have achieved major breakthroughs. In 2016, the WaveNet [3] model was proposed. It captures audio sampling points through a specially designed CNN model, converts the sequence into linear features, and automatically generates audio waveforms. It not only solved the problem of high time-resolution audio waveform modeling, but also the synthesized speech surpassed most speech synthesis models at the time, which was a groundbreaking significance. In 2017, [PERSON] et al. proposed a new end-to-end speech synthesis model: Char2 Wav [4]. Different from traditional speech synthesis models, it can directly input text for training, output acoustic features, and finally set the time window size manually to obtain the audio through SampleRNN [5]. Since then, the end-to-end speech synthesis model has been widely recognized by researchers for its unique efficiency and accuracy. In the same year, the Baidu team launched DeepVoice [6], a new end-to-end speech synthesis system built entirely using deep neural networks (DNNs) and capable of generating efficient and accurate speech. It mainly relies on the grapheme-to-phoneme model [7] and uses an audio synthesis component with a three-layer bidirectional encoder and a three-layer unidirectional decoder with a gated recurrent unit (GRU) for speech synthesis. This model has no complicated processing flow, minimizes traces of manual intervention, and lays the foundation for true end-to-end speech synthesis that can directly input characters and output speech. Subsequently, DeepVoice2 [8] and DeepVoice3 [9] were proposed one after another. The quality of synthesized audio was significantly improved compared to DeepVoice. DeepVoice3 even realized the end-to-end speech synthesis effect directly from text to speech. In 2020, [PERSON] et al. proposed a new feedforward neural network (FNN) model, namely, the ParaNet network model [10]. It can perform the parallel production of mel spectrograms by inputting text sequences, thus greatly improving the calculation efficiency and accuracy. ParaNet synthesized speech is 46-times faster than DeepVoice3 and its sound quality is clearer and more natural. Compared with DeepVoice3, its performance is even better. With the continuous development of natural language processing technology, speech synthesis technology for mainstream languages, such as Chinese and English, has made great progress, and speech synthesis for minority languages has also received more and more attention.