Improved Guided Source Separation Integrated with a Strong Back-End for the CHiME-6 Dinner Party Scenario

Abstract

The CHiME-6 dataset presents a difficult task with extreme speech overlap, severe noise and a natural speaking style. The gap of the word error rate (WER) is distinct between the au-dios recorded by the distant microphone arrays and the individual headset microphones. The official baseline exhibits a WER gap of approximately 10% even though the guided source separation (GSS) has achieved considerable WER reduction. In the paper, we make an effort to integrate an improved GSS with a strong automatic speech recognition (ASR) back-end, which bridges the WER gap and achieves substantial ASR performance improvement. Specifically, the proposed GSS is initialized by masks from data-driven deep-learning models, utilizes the spectral information and conducts a selection of the input channels. Meanwhile, we propose a data augmentation technique via random channel selection and deep convolutional neural network-based multi-channel acoustic models for back-end modeling. In the experiments, our framework largely reduced the WER to 34 . 78% / 36 . 85% on the CHiME-6 development/evaluation set. Moreover, a narrower gap of 0 . 89% / 4 . 67% was observed between the distant and headset audios. This framework is also the foundation of the IOA’s sub-mission to the CHiME-6 competition, which is ranked among the top systems.