# Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet
## 3 Mongolian Speech Synthesis Model Based on Ghost-ILPCnet
### ILPCnet Vocoder  
In the vocoder part, this paper proposed a vocoder that combined digital signal processing (DSP) and a neural network (NN) in order to improve the speed of the vocoder in generating Mongolian speech while ensuring the high quality of the generated speech [23], which added noise and filters to the Mongolian frequency band, called the ILPCnet vocoder. This structure was simple and intuitive, and greatly reduced the parameter generation. It can achieve the real-time synthesis of high-quality speech on an ordinary CPU and reduce the hardware requirements of the model. The workflow is shown in Figure 7.  
Figure 6: Ghost module stacks. ILPCnet consists of two sub-networks, the upsampling network and the waveform generation network, which are used to complete the task of matching the time resolution of the input acoustic features with the sampling rate of the speech signal, and automatically regressing to generate waveform samples. The advantage is that the linear prediction coefficient [24] is added to the neural network, making the structure simpler and easier to implement. In the upsampling network, two \(1\times 3\) convolutional layers were first used to extract the local context of the acoustic features, and the context vector was constructed from the current two frames, the past two frames, and the future two frames. Then, it was connected to the acoustic features to make the context vector more dominant for the current frame information. The fully connected layer mapped the dimensions of the context vector to the input dimensions of the context waveform generation network. Finally, the output of the fully connected layer was upsampled with the time resolution through the transposed convolutional layer, while the coefficients in the source-filter structure were predicted using a neural network, and the filters were calculated using DSP methods. In particular, the specific calculation formula is as follows, where each sampling point, \(x\), is calculated by \(p\) in the filter and excitation, \(e\). The excitation, \(e\), is predicted using a neural network and \(p\) is calculated directly according to the DSP method. The calculation process is shown in Equations (8) and (9):  
\[x_{n}=e_{n}+p_{n} \tag{8}\]  
\[p_{n}=\sum_{i=1}^{M}\alpha_{i}*x_{n-i} \tag{9}\]  
Among them, \(x_{n}\), \(e_{n}\), and \(p_{n}\) represent the \(n^{th}\) speech sample, excitation, and intermediate predictor, respectively; \(\alpha_{i}\) represents the \(i^{th}\) linear prediction coefficient, and its order is \(M\). The linear prediction filter represented the spectral components of the Mongolian speech signal well and the WaveRNN framework was used to effectively generate the remaining components of the speech signal, so the quality of the synthesized speech signal was further improved. Figure 7: Structure of ILPCnet model. According to the distribution relationship between the sampling points and excitations in Figure 7, the neural network was used to predict \(w\), \(u\), and \(s\). The specific prediction process can be observed in Equations (10) and (11):  
\[ \begin{array}{l}p(x_{n}|x_{<n},h)=\sum\limits_{i=1}^{N}w_{n,j}*\frac{1}{\sqrt{ 2\pi}s_{n,i}}\exp\left[-\frac{\left(x_{n}-\mu_{n,i}\right)^{2}}{2s_{n,i}^{2}}\right] \\ w_{n,i}^{x}=w_{n,i}^{e}\\ \mu_{n,i}^{x}=\mu_{n,i}^{e}+p_{n}\\ s_{n,i}^{x}=s_{n,i}^{e}\end{array}  \tag{10}\]  
Among them, \(x_{n}\), \(x_{<n}\), \(h\), and \(N\), respectively, represent the current Mongolian speech sample, previous speech sample, conditional acoustic features, and mixing times; \([w_{n,i}\), \(u_{n,i}\), \(s_{n,i}]\) represents the i-th mixing parameter consisting of gain, average, and scale components, respectively; and the superscripts \(e\) and \(x\) represent the stimulus and speech, respectively. Then, we used the autoregressive neural vocoder to predict the mixing parameters of the excitation signal and calculated the prediction term, \(p_{n}\), through Equation (12). And the following mixing parameters of the Mongolian speech signal could be obtained:  
\[