Abstract: The present disclosure provides acoustic model training methods and systems, and speech synthesis methods and systems. An acoustic model training method may include obtaining a plurality of training samples. Each of the plurality of training samples may include a sample text input, a sample emotion label corresponding to the sample text input, and a sample reference mel spectrum corresponding to the sample text input. The acoustic model training method may include inputting the plurality of training samples into a target model. The target model may include the acoustic model and an auxiliary module. The acoustic model training method may further include iteratively adjusting at least one model parameter of the acoustic model based on a loss target.

Abstract: The present disclosure may provide a method and a system for visual data analysis and interaction. The method includes: displaying a visualization region in a user interface, the visualization region including a set of data; generating a data graph based on the set of data; in response to receiving a first operation instruction from a user, obtaining a data processing result by processing at least a portion of the set of data based on the data graph; and in response to receiving a second operation instruction from the user, displaying the data processing result and updating a display state of the visualization region.

Abstract: The present disclosure provides acoustic model training methods and systems, and speech synthesis methods and systems. An acoustic model training method may include obtaining a plurality of training samples. Each of the plurality of training samples may include a sample text input, a sample emotion label corresponding to the sample text input, and a sample reference mel spectrum corresponding to the sample text input. The acoustic model training method may include inputting the plurality of training samples into a target model. The target model may include the acoustic model and an auxiliary module. The acoustic model training method may further include iteratively adjusting at least one model parameter of the acoustic model based on a loss target.

Abstract: The present disclosure provides systems and methods for improving a product conversion rate based on federated learning and blockchain. The system may in response to receiving a federated learning request sent by an initiator node, broadcast the federated learning request within a blockchain federation; in response to obtaining a response to the federated learning request from at least one node in the blockchain federation, determine at least one participant node; obtain first representation data related to first user data from the initiator node and second representation data related to second user data from the at least one participant node; determine a federated learning strategy corresponding to the federated learning request based on the first representation data and the second representation data; and coordinate the initiator node and the at least one participant node for federated learning based on the federated learning strategy to generate a trained conversion rate model.