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. In this paper, 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 bandwidthextended 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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. In […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"alexac"},{"type":"user_nicename","value":"mseltzer"}],"msr_publishername":"Institute of Electrical and Electronics Engineers, Inc.","msr_publisher_other":"","msr_booktitle":"Proc. of the Int. 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