Research on Emotional Speech Analysis and Synthesis

In recent years, speech analysis and synthesis technologies have achieved remarkable progress through advancements in natural language processing, signal processing, and stochastic process methodologies. This has broken through the limitations of traditional speech computation algorithms and paved the way for future developments in speech technology. The research on emotional speech analysis and synthesis is particularly significant, as it effectively integrates spoken language analysis, emotional analysis, and computer technologies. From an implementation perspective, emotional speech analysis typically involves feature extraction using digital signal processing techniques (such as spectrogram analysis and prosodic feature calculation), emotion classification through machine learning models (like CNN or LSTM networks), and synthesis modification using parameterized speech generation algorithms (including PSOLA or neural vocoders). This integration establishes a solid foundation for developing human-centered speech synthesis systems with personalized characteristics. Through emotional speech analysis and synthesis technologies, we can better understand and express human emotions, with practical applications in various fields such as human-computer interaction and voice assistants. Common implementation frameworks include using Python libraries like Librosa for feature extraction, TensorFlow/PyTorch for emotion classification models, and digital signal processing tools for real-time parameter adjustment in synthesis. Consequently, research in emotional speech analysis and synthesis demonstrates broad application prospects.