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We introduce a sophisticated multi-speaker speech data simulator,specifically engineered to generate multi-speaker speech recordings. A notablefeature of this simulator is its capacity to modulate the distribution ofsilence and overlap via the adjustment of statistical parameters. Thiscapability offers a tailored training environment for developing neural modelssuited for speaker diarization and voice activity detection. The acquisition ofsubstantial datasets for speaker diarization often presents a significantchallenge, particularly in multi-speaker scenarios. Furthermore, the precisetime stamp annotation of speech data is a critical factor for training bothspeaker diarization and voice activity detection. Our proposed multi-speakersimulator tackles these problems by generating large-scale audio mixtures thatmaintain statistical properties closely aligned with the input parameters. Wedemonstrate that the proposed multi-speaker simulator generates audio mixtureswith statistical properties that closely align with the input parametersderived from real-world statistics. Additionally, we present the effectivenessof speaker diarization and voice activity detection models, which have beentrained exclusively on the generated simulated datasets.

Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation