Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation
Tae‐Jin Park, He Huang, Coleman Richard Charles Hooper, Nithin Rao Koluguri, Kunal Dhawan, Ante Jukić, Jagadeesh Balam, Boris Ginsburg · 2023
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.