An EM algorithm for training wideband acoustic models from mixed-bandwidth training data

One serious difficulty in the deployment of wideband speech recognition systems for new tasks is the expense in both time and cost of obtaining sufficient training data. A more economical approach is to collect telephone speech and then restrict the application to operate at the telephone bandwidth. However, this generally results in suboptimal performance compared to a wideband recognition system. In this paper, we propose a novel EM algorithm in which wideband acoustic models are trained using a small amount of wideband speech augmented by a larger amount of narrowband speech. Experiments performed using wideband speech and telephone speech demonstrate that the proposed mixed-bandwidth training algorithm results in significant improvements in recognition accuracy over conventional training strategies when the amount of wideband data is limited