EEG-Guided Cantonese Speech Synthesis for Fine-Grained Emotion Modeling
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Solution Overview
Problem
Existing speech synthesis methods lack fine-grained emotional expression and are dependent on subjective emotional labeling, failing to provide differentiated expressions for specific audiences, especially in Cantonese speech for movies and television shows.
Innovation Solution
An intelligent synthesis method and system using electroencephalogram (EEG) emotion measurement to construct an EEG emotion measurement model and an emotional speech synthesis model, incorporating EEG data to optimize speech generation and enhance emotional expression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If explicit text labels are used for emotion measurement, then the modeling process is simple and easy to implement, but the emotional expression is coarse-grained and lacks fine-details
Solution Approach 1:
The patent replaces the mechanical/discrete text-labeling system with an electroencephalogram (EEG)-based physiological measurement system. EEG captures continuous brain wave patterns that objectively reflect emotional states, substituting subjective text labels with objective physiological data to achieve fine-grained emotion measurement while maintaining modeling feasibility through automated signal processing
2Ease of operation
If discrete emotion categories are used, then the measurement process is straightforward, but the emotional state representation is simplified and loses nuanced information
Solution Approach 1:
The patent changes the measurement parameter from discrete text categories to continuous EEG signal values. By measuring brain wave frequency, amplitude, and other physiological parameters continuously, the system captures subtle emotional variations and nuanced emotional states that discrete categories cannot represent, while the automated parameter extraction keeps the process operationally simple
3Ease of manufacture
If subjective text labeling is used, then the data collection process is simple, but the emotional expression is biased and lacks objectivity
Solution Approach 1:
The patent substitutes subjective human labeling with objective EEG measurements. The system automatically collects brain wave data during speech production and uses signal processing algorithms to objectively determine emotional states, eliminating researcher bias and subjective interpretation while maintaining ease of data collection through automated recording
4Device complexity
If basic emotional categories are used, then the modeling complexity is low, but the speech synthesis cannot provide differentiated expressions for specific audiences
Solution Approach 1:
The patent changes the emotion representation from basic categories to continuous EEG-based emotional features. This enables the system to capture subtle differences in emotional states across different audiences and contexts, allowing speech synthesis to adapt and differentiate expressions for specific audiences while maintaining manageable modeling complexity through automated feature extraction
Data Source
AI summary
An intelligent synthesis method and system for Cantonese speech based on electroencephalogram emotion measurement relates to the technical field of intelligent speech synthesis. The intelligent synthesis method includes: S1. acquiring data; S2. labeling data; S3. preprocessing data; S4. training an electroencephalogram emotion measurement model; S5. training an emotional speech synthesis model; and S6. performing speech synthesis. The intelligent synthesis method and system proposes an electroencephalogram emotion measurement model and an emotional speech synthesis model. The emotional speech synthesis model converts texts in a script into speeches, an audience listens to synthesized speeches when wearing a non-invasive electroencephalogram device, an electroencephalogram is generated, and the electroencephalogram generates an emotion measurement through the electroencephalogram emotion measurement model, which is conducive to optimizing speech generation under emotion measurement results and synthesizing emotionally rich speech that meets the empathy requirements of the audiences.