An experimental study on joint modeling of mixed-bandwidth data via deep neural networks for robust speech recognition

Abstract

We propose joint modeling strategies leveraging upon large-scale mixed-band training speech for recognition of both narrowband and wideband data based on deep neural networks (DNNs). We utilize conventional down-sampling and up-sampling schemes to go between narrowband and wideband data. We also explore DNN-based speech bandwidth expansion (BWE) to map some acoustic features from narrowband to wideband speech. By arranging narrowband and wideband features at the input or the output level of BWE-DNN, and combining down-sampling and up-sampling data, different DNNs can be established. Our experiments on a Mandarin speech recognition task show that the hybrid DNNs for joint modeling of mixed-band speech yield significant performance gains over both the narrowband and wideband speech models, well-trained separately, with a relative character error rate reduction of 7.9% and 3.9% on narrowband and wideband data, respectively. Furthermore, the proposed strategies also consistently outperform other conventional DNN-based methods.