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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.

In recent years, Mongolian-Chinese neural machine translation (MCNMT) technology has made substantial progress. However, the establishment of the Mongolian dataset requires a significant amount of financial and material investment, which has become a major obstacle to the performance of MCNMT. Pre-training and fine-tuning technology have also achieved great success in the field of natural language processing, but how to fully exploit the potential of pre-training language models (PLMs) in MCNMT has become an urgent problem to be solved. Therefore, this paper proposes a novel MCNMT model based on the soft target template and contextual knowledge. Firstly, to learn the grammatical structure of target sentences, a selection-based parsing tree is adopted to generate candidate templates that are used as soft target templates. The template information is merged with the encoder-decoder framework, fully utilizing the templates and source text information to guide the translation process. Secondly, the translation model learns the contextual knowledge of sentences from the BERT pre-training model through the dynamic fusion mechanism and knowledge extraction paradigm, so as to improve the model’s utilization rate of language knowledge. Finally, the translation performance of the proposed model is further improved by integrating contextual knowledge and soft target templates by using a scaling factor. The effectiveness of the modified model is verified by a large number of data experiments, and the calculated BLEU (BiLingual Evaluation Understudy) value is increased by 4.032 points compared with the baseline MCNMT model of Transformers.