Training Wideband Acoustic Models Using Mixed-Bandwidth Training Data for Speech Recognition

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 expectation-maximization (EM) algorithm in which wideband acoustic models are trained using a small amount of wideband speech and a larger amount of narrowband speech. We show how this algorithm can be incorporated into the existing training schemes of hidden Markov model (HMM) speech recognizers. 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