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In this paper, we show how uncertainty propagation, combined with observation uncertainty techniques, can be applied to a realistic implementation of robust distributed speech recognition (DSR) to improve recognition robustness furthermore, with little increase in computational complexity. Uncertainty propagation, or error propagation, techniques employ a probabilistic description of speech to reflect the information lost during speech enhancement or source separation in the time or frequency domain. This uncertain description is then propagated through the feature extraction process to the domain of features used in speech recognition. In this domain, the statistical information can be combined with the statistical parameters of the recognition model by employing observation uncertainty techniques. We show that the combination of a piecewise uncertainty propagation scheme with front-end uncertainty decoding or modified imputation improves the baseline of the advanced front-end (AFE), the state of the art algorithm of the European Telecommunications Standards Institute (ETSI), on the AURORA5 database. We compare this method with other observation uncertainty techniques and show how the use of uncertainty propagation reduces the word error rates without the need for any kind of adaptation to noise using stereo data or iterative parameter estimation.
Selected Topics in Signal Processing, IEEE Journal of (Volume:4 , Issue: 5 )
Date of Publication: Oct. 2010