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ROBUST CONTINUOUS SPEECH RECOGNITION USING PARALLEL MODEL COMBINATION
M. J. F. Gales and S. J. Young
This paper addresses the problem of automatic speech recognition in the presence of interfering noise. It focuses on the Parallel Model Combination (PMC) scheme, which has been shown to be a powerful technique for achieving noise robustness. However, most experiments reported on PMC to date have been on small, 10-50 word vocabulary systems. In this paper, PMC is applied to the Resource Management (RM) 1000 word continuous speech recognition task. This reveals compensation requirements not highlighted by the smaller vocabulary tasks, in particular, it is necessary to compensate the differential as well as the static parameters to achieve good recognition performance.
The database used for these experiments was the RM speaker independent task with Lynx helicopter noise from the NOISEX-92 database added. The experiments reported here used the HTK RM recogniser developed at CUED modified to include PMC based compensation for the static, delta and delta-delta parameters. After training on clean speech data, adding noise at 18-20dB signal to noise ratio was found to seriously degrade the performance of the recogniser. However, using PMC the performance was restored to a level comparable with that obtained when training directly in the noise corrupted environment. Additionally, PMC is shown to be robust to convolutional noise for this task.
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