  Most of the previous approaches to lyrics-to-audio alignment used a
pre-developed automatic speech recognition (ASR) system that innately suffered
from several difficulties to adapt the speech model to individual singers. A
significant aspect missing in previous works is the self-learnability of
repetitive vowel patterns in the singing voice, where the vowel part used is
more consistent than the consonant part. Based on this, our system first learns
a discriminative subspace of vowel sequences, based on weighted symmetric
non-negative matrix factorization (WS-NMF), by taking the self-similarity of a
standard acoustic feature as an input. Then, we make use of canonical time
warping (CTW), derived from a recent computer vision technique, to find an
optimal spatiotemporal transformation between the text and the acoustic
sequences. Experiments with Korean and English data sets showed that deploying
this method after a pre-developed, unsupervised, singing source separation
achieved more promising results than other state-of-the-art unsupervised
approaches and an existing ASR-based system.
