Single Channel Speech Separation Using Factorial Dynamics
Abstract
Human listeners have the extraordinary ability to hear and recognize speech even when more than one person is talking. Their machine counterparts have historically been unable to compete with this ability, until now. We present a modelbased system that performs on par with humans in the task of separating speech of two talkers from a single-channel recording. Remarkably, the system surpasses human recognition performance in many conditions. The models of speech use temporal dynamics to help infer the source speech signals, given mixed speech signals. The estimated source signals are then recognized using a conventional speech recognition system. We demonstrate that the system achieves its best performance when the model of temporal dynamics closely captures the grammatical constraints of the task. One of the hallmarks of human perception is our ability to solve the auditory cocktail party problem: we can direct our attention to a given speaker in the presence of interfering speech, and understand what was said remarkably well. Until now the same could not be said for automatic speech recognition systems. However, we have recently introduced a system which in many conditions performs this task better than humans [1][2]. The model addresses the Pascal Speech Separation Challenge task [3], and outperforms all other published results by more than 10% word error rate (WER). In this model, dynamics are modeled using a layered combination of one or two Markov chains: one for long-term dependencies and another for short-term dependencies. The combination of the two speakers was handled via an iterative Laplace approximation method known as Algonquin [4]. Here we describe experiments that show better performance on the same task with a simpler version of the model. The task we address is provided by the PASCAL Speech Separation Challenge [3], which provides standard training, development, and test data sets of single-channel speech mixtures following an arbitrary but simple