  In this paper, we tackle the singing voice phoneme segmentation problem in
the singing training scenario by using language-independent information --
onset and prior coarse duration. We propose a two-step method. In the first
step, we jointly calculate the syllable and phoneme onset detection functions
(ODFs) using a convolutional neural network (CNN). In the second step, the
syllable and phoneme boundaries and labels are inferred hierarchically by using
a duration-informed hidden Markov model (HMM). To achieve the inference, we
incorporate the a priori duration model as the transition probabilities and the
ODFs as the emission probabilities into the HMM. The proposed method is
designed in a language-independent way such that no phoneme class labels are
used. For the model training and algorithm evaluation, we collect a new jingju
(also known as Beijing or Peking opera) solo singing voice dataset and manually
annotate the boundaries and labels at phrase, syllable and phoneme levels. The
dataset is publicly available. The proposed method is compared with a baseline
method based on hidden semi-Markov model (HSMM) forced alignment. The
evaluation results show that the proposed method outperforms the baseline by a
large margin regarding both segmentation and onset detection tasks.
