Silent Speech Signal Encoding for Context-Rich Communication
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Solution Overview
Problem
Existing technologies are limited in efficiently capturing and transmitting non-speech components of communication, such as facial expressions and motor activities associated with speech, especially in environments where audible speech is difficult to produce, transmit, or understand.
Innovation Solution
A method and device for processing communication signals that involve receiving and encoding non-acoustic speech signals and non-speech signals into unique digital identifiers (UDIs), which can be transmitted and decoded to represent units of speech and non-speech information, including facial expressions and gestures, to enhance communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If audible speech is used for communication, then communication clarity is improved, but communication is difficult in environments where sound is difficult to produce, transmit or understand
Solution Approach 1:
The patent uses non-acoustic speech signals (visual, tactile, or other modalities) as an intermediary to replace audible speech in environments where sound cannot be effectively produced, transmitted, or understood. This intermediary communication channel maintains communication reliability while adapting to restrictive environments.
Solution Approach 2:
The system changes the physical parameter of speech transmission from acoustic (audible) to non-acoustic (visual, tactile, or other forms). This parameter transformation enables communication in environments where sound waves cannot propagate effectively, such as underwater, in vacuum, or in noisy conditions.
2Loss of information
If non-speech components like facial expressions and gestures are captured, then contextual information is improved, but capture and transmission efficiency deteriorates
Solution Approach 1:
The patent segments non-speech communication components into distinct units (facial expressions, gestures, body movements) and processes them separately. This segmentation allows for selective capture and efficient encoding of only the relevant contextual information, improving both information completeness and transmission efficiency.
Solution Approach 2:
The system uses a universal encoding framework that can handle multiple types of non-speech components (facial expressions, gestures, body language) through a common processing pipeline. This multi-functional approach improves efficiency by using the same capture and transmission mechanisms for diverse contextual information types.
Data Source
AI summary
The is provided a method of processing communication signals from a sender party, the method includes receiving one or more communication signals indicative of non-acousti c speech signals and/or non-speech signals from the sender party (100) processing the one or more communication signals to determine one or more communication units (102) and associating the one or more communication units with one or more unique digital identifiers (UDIs) (104).