Recognition of Continuous Broadcast News with Multiple Unknown Speakers and Environments

Practical applications of continuous speech recognition in realistic environments place increasing demands for speaker and environment independence. Until recently, this robustness has been measured using evaluation procedures where speaker and environment boundaries are known, with utterances containing complete or nearly complete sentences. This paper describes recent efforts by the CMU speech group to improve the recognition of speech found in long sections of the broadcast news show Marketplace. Most of our effort was concentrated in two areas: the automatic segmentation and classification of environments, and the construction of a suitable lexicon and language model. We review the extensions to SPHINX-II that were necessary to enable it to process continuous broadcast news and we compare the recognition accuracy of the SPHINX-II system for different environmental and speaker conditions.