Semi-supervised noise dictionary adaptation for exemplar-based noise robust speech recognition
Yi Luan, Daisuke Saito, Yosuke Kashiwagi, Nobuaki Minematsu, Keikichi Hirose · 2014
The exemplar-based approaches, which model signals as a sparse linear combination of exemplars of signals, are proved to have state-of-the-art performance in noise robust ASR, especially on low SNRs. However, since both the speech exemplars and noise exemplars are built from training data and are fixed throughout the process of enhancing speech features, the conventional approach is especially weak for unknown types of noise. Therefore, in this paper, we propose a semi-supervised approach which automatically adapt noise exemplars to the target noise, while keeping the speech exemplars fixed. Continuous digits recognition experiments show that this approach is much more robust for unknown noise. The recognition errors are reduced by 36.2%.