An Auto-Regressive, Non-Stationary Excited Signal Parameter Estimation Method and an Evaluation of a Singing-Voice Recognition
Akira Sasou, Masataka Goto, Satoru Hayamizu, Katsuhiro Tanaka · 2006
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.