首页|Data-Driven Non-Intrusive Speech Intelligibility Prediction Using Speech Presence Probability
Data-Driven Non-Intrusive Speech Intelligibility Prediction Using Speech Presence Probability
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
IEEE
Time consuming Speech Intelligibility (SI) listening tests with human subjects can be replaced by algorithmic SI predictors. In recent years, data-driven SI predictors have been showing promising results. A major limiting factor in the advancement of data-driven SI prediction is that there is a scarcity of SI listening test data available to train the data-driven methods. In this article we propose a data-driven SI predictor that does not require access to an underlying noise-free reference signal, i.e., non-intrusive, and which does not require listening test data for training. Instead, the proposed method exploits a hypothesized link between SI and Speech Presence Probability (SPP). We show that a neural network can be trained on easily obtainable speech in additive noise data to estimate SPP, and that a simple post-processing stage can be applied in order to map the estimated SPP to SI predictions with high accuracy. The proposed method is evaluated and compared to other state-of-the art non-intrusive SI predictors, and achieves the highest performance even in the presence of processed noisy speech, which the SPP estimator has not been trained on.
NETL
NSTL
IEEE
