NDLI: Factorial speech processing models for noise-robust automatic speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Khademian, M. Homayounpour, M.M.
Copyright Year	2015
Description	Author affiliation: Lab. for Intell. Multimedia Process., Amirkabir Univ. of Technol., Tehran, Iran (Khademian, M.; Homayounpour, M.M.)
Abstract	This paper presents an introduction of factorial speech processing models for noise-robust automatic speech processing tasks. Factorial models try to use more noise information rather than other robustness techniques for better generative modeling of speech and noise and the way they are combine together. Since factorial models were not completely successful in noise-robust speech processing applications while they have significant achievements in other speech processing areas in the past, we decide to reconsider them and evaluate their effects in the Aurora 2 task. In addition to Aurora noises, two more regular noises are examined in our experiments including Helicopter and Locomotive engine noises. Experiments show that these models are successful when we faced with destructive noises in addition to their unexpected improvements for non-regular non-stationary noises like Babble.
Starting Page	637
Ending Page	642
File Size	880421
Page Count	6
File Format	PDF
e-ISBN	9781479919727
DOI	10.1109/IranianCEE.2015.7146292
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-10
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision support systems Electrical engineering Radio frequency Conferences Weighted stereo sampling Two-dimensional Viterbi algorithm State-conditional observation distribution Factorial models of speech processing
Content Type	Text
Resource Type	Article

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