  This work aims to promote Chinese opera research in both musical and speech
domains, with a primary focus on overcoming the data limitations. We introduce
KunquDB, a relatively large-scale, well-annotated audio-visual dataset
comprising 339 speakers and 128 hours of content. Originating from the Kunqu
Opera Art Canon (Kunqu yishu dadian), KunquDB is meticulously structured by
dialogue lines, providing explicit annotations including character names,
speaker names, gender information, vocal manner classifications, and
accompanied by preliminary text transcriptions. KunquDB provides a versatile
foundation for role-centric acoustic studies and advancements in speech-related
research, including Automatic Speaker Verification (ASV). Beyond enriching
opera research, this dataset bridges the gap between artistic expression and
technological innovation. Pioneering the exploration of ASV in Chinese opera,
we construct four test trials considering two distinct vocal manners in opera
voices: stage speech (ST) and singing (S). Implementing domain adaptation
methods effectively mitigates domain mismatches induced by these vocal manner
variations while there is still room for further improvement as a benchmark.
