  We introduce a sophisticated multi-speaker speech data simulator,
specifically engineered to generate multi-speaker speech recordings. A notable
feature of this simulator is its capacity to modulate the distribution of
silence and overlap via the adjustment of statistical parameters. This
capability offers a tailored training environment for developing neural models
suited for speaker diarization and voice activity detection. The acquisition of
substantial datasets for speaker diarization often presents a significant
challenge, particularly in multi-speaker scenarios. Furthermore, the precise
time stamp annotation of speech data is a critical factor for training both
speaker diarization and voice activity detection. Our proposed multi-speaker
simulator tackles these problems by generating large-scale audio mixtures that
maintain statistical properties closely aligned with the input parameters. We
demonstrate that the proposed multi-speaker simulator generates audio mixtures
with statistical properties that closely align with the input parameters
derived from real-world statistics. Additionally, we present the effectiveness
of speaker diarization and voice activity detection models, which have been
trained exclusively on the generated simulated datasets.
