RC-MemNet: A Regional Context Memory Network for Cross-Regional Wu Vowel Recognition

Cross-regional Wu vowel recognition aims to identify shared vowel categories in unseen regions. Existing methods often suffer from incomplete intra-regional feature representation, insufficient inter-regional dependency modeling, and the scarcity of fine-grained cross-regional Wu vowel resources. To address these issues, we propose a Regional Context Memory Network, termed RC-MemNet, which consists of a Regional Prompt Learning (RPL) module and a Region-Guided Memory Adapter (RGMA) module. Specifically, in the RPL module, learnable regional prompts are introduced to encode region-specific acoustic characteristics through a shared encoder, providing a more complete representation of intra-regional features. Additionally, in the RGMA module, a persistent memory mechanism is introduced to propagate acoustic knowledge across source regions, supporting effective modeling of inter-regional dependencies. To increase the diversity of fine-grained cross-regional Wu vowel resources, we construct WuVowelSet, which covers 45 survey locations across 14 Wu dialect regions. Comprehensive experiments are conducted on our presented WuVowelSet dataset. The experimental results demonstrate that RC-MemNet achieves state-of-the-art performance on WuVowelSet, which is conducive to promoting the dissemination of dialect culture.