Spatial Separation of Speech Signals Using Continuously-Variable Masks Estimated From Comparisons of Zero Crossings

This paper describes an algorithm that achieves noise robustness in speech recognition by reconstructing the desired signal from a mixture of two signals using continuously-variable masks. In contrast to current methods which use binary masks, this approach estimates the relative contribution of the desired source in a mixture of sources and reconstructs the desired signal in proportion to its estimated contribution to each time-frequency segment. Estimation of the continuously-variable masks is based on the relationship between the relative intensity of each source and the interaural time difference (ITD). Estimation of the ITD is accomplished using zero-crossing-based methods. It is shown that the use of zero-crossing approaches to estimate ITDs and continuously-variable masks provide better speech recognition accuracy than cross-correlation-based approaches to ITD estimation and binary masks.