Training data selection for improving discriminative training of acoustic models

• Published in: Pattern recognition letters Vol. 30; no. 13; pp. 1228 - 1235
• Main Authors: Chen, Berlin, Liu, Shih-Hung, Chu, Fang-Hui
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2009; Elsevier
• Subjects: Acoustic models; Applied sciences; Continuous speech recognition; Data selection; Discriminative training; Entropy; Exact sciences and technology; Information, signal and communications theory; Phone accuracy; Signal processing; Speech processing; Telecommunications and information theory; Acoustic models; Data selection; Entropy; Continuous speech recognition; Discriminative training; Phone accuracy; Performance evaluation; Discriminant analysis; Automatic transcription; Cable television; Learning; Accuracy; Posterior probability; Audiovisual document; News; Speech recognition; Speech processing

This paper considers training data selection for discriminative training of acoustic models for large vocabulary continuous speech recognition (LVCSR). Three novel data selection approaches are proposed. First, the average phone accuracy over all hypothesized word sequences in the word lattice of a...