Sonority Measure for Automatic Speech Recognition

In this paper, the use of sonority measure as an acoustic feature of the speech signal for continuous automatic speech recognition is described. The representation of sonority extent of sounds is made with a help of spectrum derivation. Therefore, a novel articulatory motivated acoustic feature expressing the sonority is named spectrum derivative feature. The new feature is tested in combination with the state-ofthe-art Mel Frequency Cepstral Coefficients (MFCC) feature. The effects of various warping and filtering techniques on the spectrum derivative feature are investigated. Experiments have been performed on the large vocabulary task (VerbMobil II corpus). Improvement in word error rate has been obtained by combining the MFCC feature with the spectrum derivative feature: of up to 4.5% on the large-vocabulary task (VerbMobil II corpus) relative to using MFCC alone with the same overall number of parameters in the system.