Metaverse expands the physical world to a new dimension, and the physical environment and Metaverse environment can be directly connected and entered. Voice is an indispensable communication medium in the real world and Metaverse. Fusion of the voice with environment effects is important for user immersion in Metaverse. In this paper, we proposed using the voice conversion based method for the conversion of target environment effect speech. The proposed method was named MetaSpeech, which introduces an environment effect module containing an effect extractor to extract the environment information and an effect encoder to encode the environment effect condition, in which gradient reversal layer was used for adversarial training to keep the speech content and speaker information while disentangling the environmental effects. From the experiment results on the public dataset of LJSpeech with four environment effects, the proposed model could complete the specific environment effect conversion and outperforms the baseline methods from the voice conversion task.

Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method.的錯誤，因此需要一些調整。詳細可參考下方的程式碼。

Many measurements and searches for new phenomena performed by the experiments at the Large Hadron Collider (LHC) use a classification algorithm, such as Boosted Decision Trees or Neural Networks, to discriminate the physics process of interest (signal) from other physics processes with similar signature (background). The algorithms are optimized using supervised training on detailed simulated Monte Carlo (MC) data sets, labeled as signal or background. The resulting classifier is applied to unlabeled data to separate signal and background, and measure the statistical significance of the signal or its strength, assuming that the simulated and the real data sets are identically distributed. However, differences between real and simulated data sets always exist and the learner may pick up a discriminating feature which differs between the data sets, introducing a bias to the sample used for training. This problem is similar to that of visual recognition where training is performed on simulated pictures, the so-called source domain and applied to real photographs, the target domain. In order to avoid training specific to the source domain, algorithms of domain adaptation have been developed. In this paper, we apply the method of domain adaptation to high energy physics data. In this paper we present a Domain Adversarial Neural Network (DANN) to classify events in the search for the t tH(H b b) process at the LHC, which is very rare and hard to separate from the t t jets background [1].

Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of "deep" features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation. Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-of-the-art on Office datasets.