Subset selection for articulatory feature based confidence measures

This paper reports our recent work on optimizing the AF (articulatory features) based confidence measures, and combining them with the traditional HMM-based confidence measures. Different articulatory properties are analyzed using a separate AF-based confidence calculation method proposed in this paper, and are observed to be both complementary and redundant. A more compact subset is chosen and assembled based on the above analyses and contrast experiments, which gets a relative improvement of 12.7% on EER compared with using the whole AF set. The optimized AF-based confidence is finally combined with the HMM-based confidence, which increases the rejection rate for the out-of-vocabulary tests with no accuracy loss of the in-vocabulary tests compared with the baseline HMM system, and the relative improvement for the false acceptance rate is 34% on the development sets and 35.3% on the testing sets.