Structured Data Video Generation Using NLG and Synthetic Speech
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Solution Overview
Problem
Content creators face challenges in efficiently generating engaging videos from structured data, which is often unappealing when presented as text, and traditional video production methods are time-consuming and costly.
Innovation Solution
A computing system is used to obtain structured data, generate a textual description using a natural language generator, transform it into synthesized speech with a text-to-speech engine, and create a synthetic video that can depict a human speaking or include images and audio, leveraging deep learning techniques to make the video indistinguishable from real recordings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video production methods are used to create videos from structured data, then the videos can be engaging and high-quality, but the process is time-consuming and costly
Solution Approach 1:
The patent replaces manual video production processes with an automated system that uses natural language generation and text-to-speech technology. The computing system automatically generates textual descriptions from structured data, converts them to speech, and synthesizes videos without requiring human videographers, editors, or actors, thereby dramatically improving efficiency while reducing complexity
Solution Approach 2:
The system enables structured data to self-convert into engaging video format through automated processing. The computing system takes raw structured data, generates narratives, creates speech audio, and produces final videos autonomously, eliminating the need for external production teams and making the video generation process self-sufficient
2Ease of operation
If structured data is presented as text, then it is easily accessible and searchable, but it is unappealing to audiences
Solution Approach 1:
The patent transforms structured data from a static two-dimensional text format into a dynamic multi-dimensional video format that includes visual elements, synthesized speech, and temporal progression. This dimensional transformation maintains the accessibility of the underlying data while dramatically improving audience engagement through immersive video presentation
Solution Approach 2:
The system changes the presentation parameters of structured data by converting it from text to speech and video. The natural language generator creates narrative text, the text-to-speech engine converts it to audio with appropriate tone and pacing, and the video synthesis combines these with visual elements, transforming the data's expressive parameters to enhance appeal while preserving accessibility
Data Source
AI summary
In one aspect, an example method includes (i) obtaining, by a computing system, structured data; (ii) generating, by the computing system using a natural language generator, a textual description of the structured data; (iii) transforming, by the computing system using a text-to-speech engine, the textual description of the structured data into synthesized speech; and (iv) generating, by the computing system using the synthesized speech, a synthetic video comprising the synthesized speech.