Abstract: The present disclosure relates to systems, methods, and products for using machine-learning networks to generate trustworthy audio and face mesh. A system, serving as a digital avatar, generates a trust audio and trust face mesh corresponding to an input text. A method includes generating a set of trust embedding vectors based on a reference audio; generate a text embedding vector based on the input text; generate a conditioned vector based on the set of trust embedding vectors and the text embedding vector; synthesize an audio representation based on the conditioned vector; generate the trust audio based on the synthesized audio representation; obtain a speech feature representation based on the trust audio; obtain an abstract feature vector based on the speech feature representation; and generate positions of vertices based on the abstract feature vector, the positions of vertices being used for generating the trust face mesh.

Abstract: A device may receive raw data and metadata associated with a customer, and may transform the raw data and the metadata into unified data. The device may process the unified data, with a first model, to generate journey record derived variables, customer record derived variables, clickstream derived variables, and analytical matrices, and may process the unified data, the journey record derived variables, the customer record derived variables, the clickstream derived variables, and the analytical matrices, with a second model, to generate a journey data store with journey derived signals. The device may process the unified data, the journey record derived variables, the customer record derived variables, the clickstream derived variables, the analytical matrices, and the journey data store, with a third model, to generate overall statistical data for the customer, and may perform one or more actions based on the overall statistical data.