Humming-based human verification and identification

This paper considers humming-based systems for human verification and identification. Humming of a target person is modeled as a Gaussian mixture model, and the matching score between a target model and humming is computed as the likelihood of humming given a target model. Verification is performed by comparing the matching score to the likelihood given a universal background model, and identification is performed by selecting the best-matched model. The verification and identification performances are evaluated using various acoustical features. The experimental results show that linear prediction cepstral coefficients and perceptually linear prediction coefficients are conducive to verification and identification, respectively.