Analysis and subset selection of articulatory features for speech recognition confidence measures

Different articulatory properties are analyzed in terms of confidence measures using a separate AF-based confidence calculation method.The analysis not only verifies the necessity of assembly,but also demonstrates a great deal of redundancies between the articulatory properties and HMM.In order to reduce the redundancy,a subset selection method is proposed.Experiments are designed to verify the above assumptions.Compared with all used together,the confidence measures based on the compact subset of articulatory features get a relative decrease of 12.7%for EER.The optimized AF-based confidence is finally combined with the HMM-based confidence,and increases rejection rate for the out of vocabulary tests with no accuracy loss of the in vocabulary tests,and the relative improvement is 34%on the development sets and 35.3%on the testing sets with the same parameters.