Broadcasting voice is used to convey ideas and emotions. In the selection process of broadcasting and hosting professionals, the vocal timbre is an important index. The subjective evaluation method is widely used, but the selection results have certain subjectivity and uncertainty. In this paper, an objective evaluation method of broadcasting vocal timbre is proposed. Firstly, the broadcasting vocal timbre database is constructed on Chinese phonetic characteristics. Then, the timbre feature selection strategy is presented based on human vocal mechanism, and the broadcast timbre characteristics are divided into three categories, which include source parameters, vocal tract parameters, and human hearing parameters. Finally, the three models of hidden Markov model (HMM), Gaussian Mixture Model-General Background Model (GMM-UBM), and long short-term memory (LSTM) are exploited to evaluate the timbre of the broadcast by extracting timbre features and four timbre feature combinations. The experiments show that the selection of timbre features is scientific and effective. Moreover, the accuracy of the LSTM network using the deep learning algorithm in the objective evaluation of the broadcast timbre is better than the traditional HMM and GMM-UBM, and the proposed method can achieve about 95 accuracy rate in our database.

In the metaverse, video text retrieval is an urgent and challenging need for users in social entertainment. The current attention‐based video text retrieval models have not fully explored the interaction between video and text, and only brute force feature embedding. Moreover, Due to the unsupervised nature of attention weight training, existing models have weak generalization performance for dataset bias. Essentially, the model learns that false relevant information in the data is caused by confounding factors. Therefore, this article proposes a video text retrieval method based on causal attention transformer. Assuming that the confounding factors affecting the performance of video text retrieval all come from the dataset, a structural causal model that conforms to the video text retrieval task is constructed, and the impact of confounding effects during data training is reduced by adjusting the front door. In addition, we use causal attention transformer to construct a causal inference network to extract causal features between video text pairs, and replace the similarity statistical probability with causal probability in the video text retrieval framework. Experiments are conducted on the MSR‐VTT, MSVD, and LSMDC datasets, which proves the effectiveness of the retrieval model proposed in this paper.

The core challenge in video-text retrieval lies in measuring the cross-modal gap between visual and linguistic representations. While mainstream methods leverage deep learning to project video and text features into a shared space for similarity matching, they often rely on superficial statistical correlations. This limits their ability to model underlying causal relationships or capture high-level semantics, resulting in poor interpretability. To address this, we analyze the biases existing in video-text retrieval tasks and introduce a Causal Visual–Semantic Enhancement (CVSE) method that integrates causal inference with deep learning. Our approach applies causal intervention to mitigate bias from contextual confounders and uses textual descriptions as a condition to guide frame aggregation, emphasizing semantically relevant frames while suppressing redundant ones. Experiments on MSR-VTT, MSVD, and LSMDC demonstrate that the proposed method outperforms state-of-the-art retrieval models, validating its superior performance.