  This paper introduces GLOBE, a high-quality English corpus with worldwide
accents, specifically designed to address the limitations of current zero-shot
speaker adaptive Text-to-Speech (TTS) systems that exhibit poor
generalizability in adapting to speakers with accents. Compared to commonly
used English corpora, such as LibriTTS and VCTK, GLOBE is unique in its
inclusion of utterances from 23,519 speakers and covers 164 accents worldwide,
along with detailed metadata for these speakers. Compared to its original
corpus, i.e., Common Voice, GLOBE significantly improves the quality of the
speech data through rigorous filtering and enhancement processes, while also
populating all missing speaker metadata. The final curated GLOBE corpus
includes 535 hours of speech data at a 24 kHz sampling rate. Our benchmark
results indicate that the speaker adaptive TTS model trained on the GLOBE
corpus can synthesize speech with better speaker similarity and comparable
naturalness than that trained on other popular corpora. We will release GLOBE
publicly after acceptance. The GLOBE dataset is available at
https://globecorpus.github.io/.
