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Paper Detail

Presentation #	7
Session:	ASR I
Session Time:	Wednesday, December 19, 10:00 - 12:00
Presentation Time:	Wednesday, December 19, 10:00 - 12:00
Presentation:	Poster
Topic:	Speech recognition and synthesis:
Paper Title:	IMPROVING LF-MMI USING UNCONSTRAINED SUPERVISIONS FOR ASR
Authors:	Hossein Hadian; Sharif University of Technology
	Daniel Povey; Johns Hopkins University
	Hossein Sameti; Sharif University of Technology
	Jan Trmal; Johns Hopkins University
	Sanjeev Khudanpur; Johns Hopkins University
Abstract:	We present our work on improving the numerator graph for discriminative training using the lattice-free maximum mutual information (MMI) criterion. Specifically, we propose a scheme for creating unconstrained numerator graphs by removing time constraints from the baseline numerator graphs. This leads to much smaller graphs and therefore faster preparation of training supervisions. By testing the proposed unconstrained supervisions using factorized time-delay neural network (TDNN) models, we observe 0.5\% to 2.6\% relative improvement over the state-of-the-art word error rates on various large-vocabulary speech recognition databases.