Emotion Classification Text-to-Speech Semantic Analysis
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Solution Overview
Problem
Conventional Text-To-Speech (TTS) systems fail to effectively convey the intent and emotion of a user's message, lacking the ability to analyze semantic content and context to produce emotionally nuanced speech outputs.
Innovation Solution
An emotion classification information-based TTS method that determines whether emotion metadata is set in a message, using semantic and context analysis to generate metadata for the speech synthesis engine, allowing it to produce speech that reflects the intended emotion, even if no explicit emotion classification is provided.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional TTS processing is used to transmit semantic contents, then the basic text-to-speech function is achieved, but the ability to convey user intent and emotion is lost
Solution Approach 1:
The system segments the TTS processing into distinct modules: a semantic analysis module that extracts emotion information from text, a context analysis module that considers conversational context, and a speech synthesis module that generates emotionally nuanced speech. This segmentation allows each module to specialize in one aspect of emotion detection and speech generation, reducing overall system complexity while preserving emotion information.
Solution Approach 2:
The system performs preliminary semantic and context analysis on the input text before speech synthesis to pre-determine the appropriate emotion classification. By preparing emotion metadata in advance through analysis of the message content and context, the system ensures emotion information is captured before the speech generation process begins, preventing loss of emotional nuance.
2Reliability
If emotion classification information is manually set in messages, then speech with intended emotion can be produced, but user operation complexity increases
Solution Approach 1:
The system automatically performs semantic and context analysis on user messages to self-determine the appropriate emotion classification without requiring manual user input. The semantic analysis module extracts emotional cues from the message content, while the context analysis module considers the conversational context, allowing the system to autonomously generate accurate emotion metadata that reflects the user's intended emotion.
Solution Approach 2:
The system uses feedback from semantic analysis results and context analysis results to automatically adjust and determine the most appropriate emotion classification. By analyzing the message content and comparing it against known emotional patterns and contextual cues, the system refines its emotion determination to accurately reflect user intent without manual intervention.
3Measurement precision
If semantic analysis and context analysis are performed to generate emotion metadata, then speech synthesis accuracy is improved, but processing time increases
Solution Approach 1:
The system performs partial semantic analysis and context analysis focused specifically on extracting emotion-relevant features from the message, rather than comprehensive analysis of all text characteristics. By concentrating computational resources on emotion-specific linguistic cues and contextual indicators, the system achieves sufficient emotion detection precision without the time cost of exhaustive analysis.
Data Source
AI summary
Disclosed are an emotion classification information-based text-to-speech (TTS) method and device. The emotion classification information-based TTS method according to an embodiment of the present invention may, when emotion classification information is set in a received message, transmit metadata corresponding to the set emotion classification information to a speech synthesis engine and, when no emotion classification information is set in the received message, generate new emotion classification information through semantic analysis and context analysis of sentences in the received message and transmit the metadata to the speech synthesis engine. The speech synthesis engine may perform speech synthesis by carrying emotion classification information based on the transmitted metadata.