Emotion Metadata for Voice-Text Channel Preservation
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Solution Overview
Problem
Current technologies fail to effectively preserve and translate the emotional content of voice and text communications across different languages and cultures, leading to loss of emotional nuance in communication transformations.
Innovation Solution
A system that analyzes voice and text communications to extract emotional metadata, translates this metadata into target languages, and adjusts voice synthesis to match the emotional tone of the original communication, using emotion dictionaries and context profiles to ensure emotional consistency across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If voice communication is transformed into text using automatic speech recognition, then text content can be extracted and processed, but emotional content and delivery characteristics are lost
Solution Approach 1:
The patent segments the speech signal into two independent components: phonemic content (for text recognition) and emotional delivery characteristics (for emotion preservation). This allows text extraction while maintaining emotional information through separate processing channels, resolving the contradiction between text processing capability and emotional content preservation
Solution Approach 2:
The patent introduces emotion metadata as an intermediary element that bridges the gap between voice communication and text representation. This metadata carries emotional delivery characteristics and is attached to the transcribed text, enabling emotional content to be preserved and transmitted across modalities without sacrificing text processing capabilities
2Measurement precision
If emotion recognition systems analyze acoustic representations of sub-emotion units, then emotional state can be identified, but the complexity of the analysis increases
Solution Approach 1:
The patent extracts emotional delivery characteristics from the full speech signal by focusing on specific acoustic features (pitch, tone, cadence, amplitude) that carry emotional information. This extraction approach isolates the emotional component from the phonemic content, enabling accurate emotion recognition while reducing overall analysis complexity through targeted feature selection
Solution Approach 2:
The patent transforms the complex acoustic signal into simplified emotional parameters (sub-emotion units) that represent emotional state. By changing the representation from raw acoustic waveforms to standardized emotional parameters, the system achieves high emotion recognition accuracy while reducing the complexity of subsequent processing and computation
3Measurement precision
If speech is filtered into gender-neutral monotonic audio stream for text recognition, then word recognition accuracy improves, but emotional delivery characteristics are altered
Solution Approach 1:
The patent segments the speech processing into two parallel paths: one that filters and processes phonemic content for accurate word recognition, and another that preserves and analyzes emotional delivery characteristics. This segmentation allows each path to optimize for its specific function without compromising the other, resolving the contradiction between recognition accuracy and delivery characteristic preservation
Solution Approach 2:
The patent introduces emotion metadata as an intermediary that captures delivery characteristics without interfering with the text recognition process. This metadata serves as a bridge that conveys emotional information while allowing the phonemic processing to proceed independently with optimal filtering and normalization for accurate word recognition
4Productivity
If text translation is performed without emotion metadata, then translation speed increases, but emotional nuance and cultural context are lost
Solution Approach 1:
The patent performs preliminary emotion extraction and metadata generation on the source language speech before translation occurs. This preliminary action captures emotional delivery characteristics in advance, allowing them to be attached to the translated text without adding computational delay to the translation process itself, thus maintaining high productivity while preserving emotional nuance
Solution Approach 2:
The patent uses emotion metadata as an intermediary that travels with the text through the translation process. This metadata is attached to the source text, processed through translation, and attached to the target text, enabling emotional nuance to be preserved across languages without requiring complex emotion translation algorithms that would slow down the process
Data Source
AI summary
Communicating across channels with emotion preservation includes: receiving, by a processor in a communication device, a voice communication; analyzing, by the processor in the communication device, the voice communication for first emotion content; analyzing, by the processor in the communication device, textual content of the voice communication for second emotion content; and marking up, by the processor in the communication device, the textual content with emotion metadata for one of the first emotion content and the second emotion content.