Noise Adaptive Stream Fusion Based on Feature Component Rejection for Robust Multi-Stream Speech Recognition

Weighting the stream outputs according to their reliability levels is one of the most common stream fusion methods in the multi-stream automatic speech recognition (MS ASR). However, when a MS ASR system works in noisy environments, there are distortion level differences among not only the data streams, but also the feature components inside a stream. In this paper, we first propose a feature component rejection approach that can provide the similar function as the missing data techniques while is much easier to be applied to different features. Then a new stream fusion method that can make use of the reliability information of both inter-and intra-streams is developed by incorporating the proposed feature component rejection approach into the conventional MS HMM. The proposed stream fusion method shows good noise adaptive ability and achieves similar recognition accuracy as the missing data based stream fusion method for additive noises in the experiments of the Ti digits connected word recognition task.