Research on Emotional Speech Analysis and Synthesis
In recent years, speech analysis and synthesis technologies have achieved significant advancements driven by methodologies from natural language processing, signal processing, and stochastic process analysis, surpassing traditional speech computation algorithms. Emotional speech analysis and synthesis represents the future development trend of speech technology, as it effectively integrates speech analysis, emotional analysis, and computer technology. Key implementation approaches include using spectral features extraction (e.g., MFCCs) for acoustic analysis, employing machine learning classifiers (SVMs or neural networks) for emotion recognition, and modifying prosodic parameters (pitch, duration, intensity) through digital signal processing algorithms for emotional synthesis. This research lays the foundation for human-centered, personalized speech synthesis systems.