AN UNSUPERVISED LEARNING APPROACH TO NEURAL-NET-SUPPORTED WPE DEREVERBERATION

Reverberation degrades signal quality and increases word error rates in automatic speech recognition (ASR). Reverberation suppression is, thus, a key component in listening enhancement devices and ASR front end. The weighted prediction error (WPE) is a prominent and effective method that gained popularity in recent ASR challenges. The need for iterative optimization in WPE leads to high computational cost and instabilities for short signals. Neural net (NN) supported WPE was proposed to alleviate these issues. However, NN training requires parallel data, i.e., reverberant and "clean" (direct sound plus early reflections) speech, which is not available in general. We show that the supporting network can be trained efficiently, without any supervision, using reverberant speech only. Consequently, adaptation to unseen environments is largely simplified. Network training involves the complete de-reverberation system and relies on complex-valued back propagation. The experimental validation confirms that, the proposed approach matches the performance of the method with parallel training data both in terms of perceptual quality and ASR word error rates.