Emotional Speech Synthesis Based on Valence-Arousal-Dominance Model and Multi-Feature Codebook

Despite significant advancements in emotional speech synthesis technology, existing synthesis systems still face challenges in achieving fine-grained emotional expressiveness while maintaining computational efficiency. To address these limitations, we propose a novel speech synthesis framework based on the Valence-Arousal-Dominance (VAD) three-dimensional emotional model, which integrates continuous emotion feature extraction with multi-feature codebook. Within this framework, we first construct a multi-dimensional codebook encompassing both acoustic and emotional features by extracting VAD characteristics from training set audio samples. An intelligent retrieval strategy is then employed to train a codebook matcher for optimal feature matching. Additionally, the text encoder is enhanced by incorporating adaptive normalization layers, rotary positional encoding, and local attention mechanisms. Experimental results demonstrate that the proposed framework significantly enhances the emotional expressiveness of synthesized speech while maintaining computational efficiency.