## 3 Methods  
The speech synthesized by the VITS model lacks naturalness and prosody. Through spectrogram comparison analysis, whose results are shown in the last figure, it was observed that while the synthesized audio closely matches the original in the low-frequency range, significant discrepancies exist in the mid- and high-frequency regions, where many details remain unrecovered. The VITS model struggles to effectively learn the mid- and high-frequency components of the speech data in the dataset, resulting in synthesized speech that fails to accurately reproduce prosody. After a careful analysis, we discovered that VITS can be seen as a variational autoencoder (VAE) architecture network. Its prior and posterior encoders extract the prior and posterior distributions of latent variables \(z\) from text and spectrograms, respectively. These distributions are then aligned and fed into a vocoder to reconstruct the audio. The final optimization objective is not the log-likelihood of the latent variable \(z\), but rather the maximization of the Evidence Lower Bound (ELBO). To enhance the expressiveness and effectiveness of VITS speech synthesis, considerable effort has been directed towards improving the capability of the prior encoder to extract textual features or optimizing alignment algorithms to enhance alignment accuracy. However, there has been no related work focused on modifying the posterior encoder to improve the extraction efficiency of spectrogram features. As the prior encoder in VITS is essentially a text encoder, with the remarkable performance of pre-trained text encoders like BERT in the NLP domain, related work targeting the prior encoder of the VITS model has proposed corresponding pre-trained models. For instance, pre-trained models such as XPhoneBERT have been introduced specifically for phoneme-based applications. At the same time, since the prior encoder handles phonemes rather than raw text, there are also related studies suggesting that using pseudo-phonemes instead of raw text as input can improve the model's expressiveness. However, these approaches either require significant time to collect and process corresponding corpora (XPhoneBERT necessitates a large volume of text converted into phoneme sequences as input and adopts a structure similar to BERT, thus requiring extensive pre-training time) or entail extensive data preprocessing (the pseudo-phoneme approach involves multiple rounds of clustering and merging of all pseudo-phonemes and then the hidden representation in the entire dataset), making training quite complex. Our proposed model architecture is shown in Figures 1 and 2. Figure 1 illustrates the structure of the model during the training phase. The model receives text sequences and the corresponding waveforms as input. The prior encoder, also known as the text encoder, is responsible for computing the prior distribution of the latent variable \(z\). The posterior encoder is responsible for computing the posterior distribution of the latent variable \(z\) from the spectrogram obtained through the Fast Fourier Transform (FFT) of the waveform. The alignment algorithm calculates the alignment matrix between the spectrogram and the text. The decoder samples from the posterior distribution to predict the waveform. The duration predictor learns the duration of the corresponding text in the waveform from the alignment matrix. Figure 2 shows the structure of the model during the inference phase. During inference, the model receives text sequences as input. After processing is concluded by the prior encoder, the model samples are based on the duration predictor's instructions. This is followed by normalization flow. Then, the samples are passed to the posterior encoder, and, finally, the decoder generates the waveform. The original posterior encoder in VITS takes linear spectrogram input \(x\) and possible conditional input \(g\) and then performs forward propagation operations. Firstly, if there is a conditional input \(g\), it is processed through conditional layers.