Writing is an important carrier of cultural inheritance, and the digitization of handwritten texts is an effective means to protect national culture. Compared to Chinese and English handwriting recognition, the research on Mongolian handwriting recognition started relatively late and achieved few results due to the characteristics of the script itself and the lack of corpus. First, according to the characteristics of Mongolian handwritten characters, the random erasing data augmentation algorithm was modified, and a dual data augmentation (DDA) algorithm was proposed by combining the improved algorithm with horizontal wave transformation (HWT) to augment the dataset for training the Mongolian handwriting recognition. Second, the classical CRNN handwriting recognition model was improved. The structure of the encoder and decoder was adjusted according to the characteristics of the Mongolian script, and the attention mechanism was introduced in the feature extraction and decoding stages of the model. An improved handwriting recognition model, named the EGA model, suitable for the features of Mongolian handwriting was suggested. Finally, the effectiveness of the EGA model was verified by a large number of data tests. Experimental results demonstrated that the proposed EGA model improves the recognition accuracy of Mongolian handwriting, and the structural modification of the encoder and coder effectively balances the recognition accuracy and complexity of the model.

The core challenge of speech synthesis technology is how to convert text information into an audible audio form to meet the needs of users. In recent years, the quality of speech synthesis based on end-to-end speech synthesis models has been significantly improved. However, due to the characteristics of the Mongolian language and the lack of an audio corpus, the Mongolian speech synthesis model has achieved few results, and there are still some problems with the performance and synthesis quality. First, the phoneme information of Mongolian was further improved and a Bang-based pre-training model was constructed to reduce the error rate of Mongolian phonetic synthesized words. Second, a Mongolian speech synthesis model based on Ghost and ILPCnet was proposed, named the Ghost-ILPCnet model, which was improved based on the Para-WaveNet acoustic model, replacing ordinary convolution blocks with stacked Ghost modules to generate Mongolian acoustic features in parallel and improve the speed of speech generation. At the same time, the improved vocoder ILPCnet had a high synthesis quality and low complexity compared to other vocoders. Finally, a large number of data experiments were conducted on the proposed model to verify its effectiveness. The experimental results show that the Ghost-ILPCnet model has a simple structure, fewer model generation parameters, fewer hardware requirements, and can be trained in parallel. The average subjective opinion score of its synthesized speech reached 4.48 and the real-time rate reached 0.0041. It ensures the naturalness and clarity of synthesized speech, speeds up the synthesis speed, and effectively improves the performance of the Mongolian speech synthesis model.

Mongolian speech synthesis is a technology that converts Mongolian text into Mongolian speech. In order to improve the emotional expressiveness of synthesized speech, this article first proposed a lightweight Mongolian phoneme pre-training model WFST-MnG2P based on weighted finite state transition machine. Secondly, as a representative low-resource language, Mongolian currently has no open source emotional speech corpus. For this reason, a Mongolian emotional speech corpus containing seven discrete emotions was constructed, totaling about 2.25 hours. Finally, since the non-autoregressive acoustic model can reduce word skipping, word missing, repeated pronunciation, and so on, and speed up the speech synthesis speed, this article proposes a Mongolian emotional speech synthesis model based on conditional generative adversarial network and improved FastSpeech2. Experimental results show that the average MOS score of emotional speech on the self-built Mongolian emotional speech corpus is 3.69, and the model can synthesize Mongolian emotional speech with rich multi-dimensional emotions and more robustness.