In this study, hybrid state-action-reward-state-action (SARSAλ) and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSAλ and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results show that hybrid online and offline reinforcement learning with a deep neural network can improve the game program’s learning efficiency and understanding ability for Tibetan Jiu chess.

The research on Tibetan speech synthesis technology has been mainly focusing on single dialect, and thus there is a lack of research on Tibetan multidialect speech synthesis technology. This paper presents an end‐to‐end Tibetan multidialect speech synthesis model to realize a speech synthesis system which can be used to synthesize different Tibetan dialects. Firstly, Wylie transliteration scheme is used to convert the Tibetan text into the corresponding Latin letters, which effectively reduces the size of training corpus and the workload of front‐end text processing. Secondly, a shared feature prediction network with a cyclic sequence‐to‐sequence structure is built, which maps the Latin transliteration vector of Tibetan character to Mel spectrograms and learns the relevant features of multidialect speech data. Thirdly, two dialect‐specific WaveNet vocoders are combined with the feature prediction network, which synthesizes the Mel spectrum of Lhasa‐Ü‐Tsang and Amdo pastoral dialect into time‐domain waveform, respectively. The model avoids using a large number of Tibetan dialect expertise for processing some time‐consuming tasks, such as phonetic analysis and phonological annotation. Additionally, it can directly synthesize Lhasa‐Ü‐Tsang and Amdo pastoral speech on the existing text annotation. The experimental results show that the synthesized speech of Lhasa‐Ü‐Tsang and Amdo pastoral dialect based on our proposed method has better clarity and naturalness than the Tibetan monolingual model.

Speech-to-speech translation (S2ST) has evolved from cascade systems which integrate Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS), to end-to-end models. This evolution has been driven by advancements in model performance and the expansion of cross-lingual speech datasets. Despite the paucity of research on Tibetan speech translation, this paper endeavors to tackle the challenge of Tibetan-to-Chinese direct speech-to-speech translation within the multi-task learning framework, employing self-supervised learning (SSL) and sequence-to-sequence model training. Leveraging HuBERT model to extract discrete units of target speech, we develop a speech-to-unit translation (S2UT) model using an encoder-decoder architecture which subsequently generates speech output through a unit-based vocoder. By employing SSL and utilizing discrete representations as training targets, our approach effectively captures linguistic differences, facilitating direct translation between the two languages. We evaluate the performance of HuBERT model under various configurations to select the optimal setup based on Phone-unit Normalized Mutual Information (PNMI) values. After fine-tuning the chosen HuBERT model on specific corpora, we introduce auxiliary tasks to enhance translation performance. This underscores the pivotal role of multi-task learning in improving overall model efficacy. Experimental results validate the feasibility of Tibetan-to-Chinese S2ST, demonstrating promising translation quality and semantic content preservation, despite limited data availability.

Effective supervision quality control during the construction project implementation is an important measure to ensure the overall construction quality of the project. The effective implementation of the supervision work can greatly improve the overall construction effect of the construction project, can guarantee the construction quality of the project, and at the same time can strictly control the construction cost, construction safety and construction progress. Therefore, the quality control of engineering projects has a crucial role in the construction process of construction projects. This requires an analysis based on the importance of supervision quality control work and the current research status. At the same time, the key points of supervision quality control during the construction process of construction are put forward, so as to give full play to the positive role of supervision quality control work in construction projects and enhance construction projects’ construction level.

Most current research in Tibetan speech synthesis relies primarily on autoregressive models in deep learning. However, these models face challenges such as slow inference, skipped readings, and repetitions. To overcome these issues, we propose an enhanced non-autoregressive acoustic model combined with a vocoder for Tibetan speech synthesis. Specifically, we introduce the mixture alignment FastSpeech2 method to correct errors caused by hard alignment in the original FastSpeech2 method. This new method employs soft alignment at the level of Latin letters and hard alignment at the level of Tibetan characters, thereby improving alignment accuracy between text and speech and enhancing the naturalness and intelligibility of the synthesized speech. Additionally, we integrate pitch and energy information into the model, further enhancing overall synthesis quality. Furthermore, Tibetan has relatively smaller text-to-audio datasets compared to widely studied languages. To address these limited resources, we employ a transfer learning approach to pre-train the model with data from resource-rich languages. Subsequently, this pre-trained mixture alignment FastSpeech2 model is fine-tuned for Tibetan speech synthesis. Experimental results demonstrate that the mixture alignment FastSpeech2 model produces higher-quality speech compared to the original FastSpeech2 model, particularly when pre-trained on an English dataset, resulting in further improvements in clarity and naturalness.