Robust continuous speech recognition using parallel model combination

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. Most experiments reported on PMC to date have been on small, 10-50 word vocabulary systems. Experiments on the Resource Management (RM) database, a 1000 word continuous speech recognition task, reveal compensation requirements not highlighted by the smaller vocabulary tasks. In particular, that it is necessary to compensate the dynamic parameters as well as the static parameters to achieve good recognition performance. The database used for these experiments was the RM speaker independent task with either Lynx Helicopter noise or Operation Room noise from the NOISEX-92 database added. The experiments reported here used the HTK RM recognizer developed at CUED modified to include PMC based compensation for the static, delta and delta-delta parameters. After training on clean speech data, the performance of the recognizer was found to be severely degraded when noise was added to the speech signal at between 10 and 18 dB. However, using PMC the performance was restored to a level comparable with that obtained when training directly in the noise corrupted environment.