Humming-based human verification and identification

This paper considers humming-based human verification and identification systems. 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.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Minho Jin, Jaewook Kim, Chang D. Yoo
• Keywords: Gaussian Mixture Model, ICASSP 2009, Matching Score, Signal Processing, Target Model