Please use this identifier to cite or link to this item: https://doi.org/10.1109/ASRU.2011.6163914
Title: A trajectory-based parallel model combination with a unified static and dynamic parameter compensation for noisy speech recognition
Authors: Sim, K.C. 
Luong, M.-T. 
Keywords: Noise Robustness
Parallel Model Combination
Trajectroy HMM
Issue Date: 2011
Source: Sim, K.C.,Luong, M.-T. (2011). A trajectory-based parallel model combination with a unified static and dynamic parameter compensation for noisy speech recognition. 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings : 107-112. ScholarBank@NUS Repository. https://doi.org/10.1109/ASRU.2011.6163914
Abstract: Parallel Model Combination (PMC) is widely used as a technique to compensate Gaussian parameters of a clean speech model for noisy speech recognition. The basic principle of PMC uses a log normal approximation to transform statistics of the data distribution between the cepstral domain and the linear spectral domain. Typically, further approximations are needed to compensate the dynamic parameters separately. In this paper, Trajectory PMC (TPMC) is proposed to compensate both the static and dynamic parameters. TPMC uses the explicit relationships between the static and dynamic features to transform the static and dynamic parameters into a sequence (trajectory) of static parameters, so that the log normal approximation can be applied. Experimental results on WSJCAM0 database corrupted with additive babble noise reveals that the proposed TPMC method gives promising improvements over PMC and VTS. © 2011 IEEE.
Source Title: 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/41893
ISBN: 9781467303675
DOI: 10.1109/ASRU.2011.6163914
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