Training Wideband Acoustic Models using Mixed-Bandwidth Training Data via Feature Bandwidth Extension
Michael L. Seltzer, Alex Acero · 2006
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 sub-optimal performance. We propose a new algorithm for training wideband acoustic models that requires only a small amount of wideband speech augmented by a larger amount of narrowband speech. The algorithm operates by first converting the narrowband features to wideband features through a process called feature bandwidth extension. The bandwidth-extended features are then combined with available wideband data to train the acoustic models using a modified version of the conventional forward-backward algorithm. 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.