System

A system comprising:

• circuitry configured to:
• receive, via a communication interface coupled to a packet-switched network, acoustic information from a terminal device, apply a signal processing pipeline to compute acoustic feature quantities comprising pitch contours, speaking rate statistics, intonation profiles, intensity envelopes, and pausing patterns, and structure the computed quantities as non-personal acoustic feature data by replacing source identifiers with an anonymized session identifier;
• apply a natural language processing pipeline comprising tokenization, part-of-speech tagging, dependency parsing, and vocabulary frequency analysis to document data to extract document feature quantities, and compute writing style feature vectors using term frequency-inverse document frequency weighting and syntactic complexity statistics;
• apply a convolutional neural network to image data to extract appearance feature vectors and generate virtual person generation data comprising a parametric appearance model, a motion behavior model derived from the non-personal acoustic feature data, and a speech behavior model derived from the acoustic feature quantities;
• generate a prompt sentence embedding the writing style feature vectors and syntactic structure features as structured input sections and input the prompt sentence to a generative neural network model to produce generated document data conditioned on lexical density and syntactic complexity targets;
• and
• associate the generated document data with the virtual person generation data by synchronizing text tokens with animation frame intervals derived from the motion behavior model, and store the associated data in a storage device.

A method comprising:

• receiving, via a communication interface coupled to a packet-switched network, acoustic information from a terminal device, converting the acoustic information into acoustic data excluding personally identifiable information, and applying an acoustic analysis program to compute acoustic feature quantities comprising pitch, speaking rate, intonation, intensity, and pausing pattern;
• applying a document analysis program to document data to extract document feature quantities indicative of writing style, vocabulary selection, and syntactic structure;
• applying an image analysis program to image data to extract appearance feature quantities and generating virtual person generation data based on the appearance feature quantities, the acoustic feature quantities, and the document feature quantities;
• generating a prompt sentence using the document feature quantities and inputting the prompt sentence to a generative neural network model to produce generated document data reflecting the document feature quantities;
• and
• storing the generated document data in association with the virtual person generation data.