Towards Generalizing Classification Based Speech Separation

Monaural speech separation is a well-recognized challenge. Recent studies utilize supervised classification methods to estimate the ideal binary mask (IBM) to address the problem. In a supervised learning framework, the issue of generalization to conditions different from those in training is very important. This paper presents methods that require only a small training corpus and can generalize to unseen conditions. The system utilizes support vector machines to learn classification cues and then employs a rethresholding technique to estimate the IBM. A distribution fitting method is used to generalize to unseen signal-to-noise ratio conditions and voice activity detection based adaptation is used to generalize to unseen noise conditions. Systematic evaluation and comparison show that the proposed approach produces high quality IBM estimates under unseen conditions.