Phone level confidence measure using articulatory features

Confidence measures are used in a number of applications to verify the user input or to measure the certainty of the recognition outputs. Most of the HMM-based systems use MFCC features with Gaussian mixtures models to estimate confidence. In this paper, we propose a new approach to estimate confidence by combining the posterior probabilities of articulatory features (AF) computed by a set of AF classifiers. This AF-based confidence measure gives comparable performance in terms of classification equal error rate (EER) to the Gaussian mixture-based approach but reduces the computation by 50% (as measured by the approximated number of multiplications) and consumes smaller memory. When the AF-based confidence is combined with confidence from the Gaussian mixtures, the EER is further reduced. This AF confidence can be particularly useful for platforms with limited computing resources such as hand-held devices.