An auto-regressive, non-stationary excited signal parameter estimation method and an evaluation of a singing-voice recognition

We have previously described an Auto-Regressive Hidden Markov Model (AR-HMM) and an accompanying parameter estimation method. The AR-HMM was obtained by combining an AR process with an HMM introduced as a non-stationary excitation model. We demonstrated that the AR-HMM can accurately estimate the characteristics of both articulatory systems and excitation signals from high-pitched speech. In this paper, we apply the AR-HMM to feature extraction from singing voices and evaluate the recognition accuracy of the AR-HMM-based approach.