  In recent years, the accuracy of automatic lyrics alignment methods has
increased considerably. Yet, many current approaches employ frameworks designed
for automatic speech recognition (ASR) and do not exploit properties specific
to music. Pitch is one important musical attribute of singing voice but it is
often ignored by current systems as the lyrics content is considered
independent of the pitch. In practice, however, there is a temporal correlation
between the two as note starts often correlate with phoneme starts. At the same
time the pitch is usually annotated with high temporal accuracy in ground truth
data while the timing of lyrics is often only available at the line (or word)
level. In this paper, we propose a multi-task learning approach for lyrics
alignment that incorporates pitch and thus can make use of a new source of
highly accurate temporal information. Our results show that the accuracy of the
alignment result is indeed improved by our approach. As an additional
contribution, we show that integrating boundary detection in the
forced-alignment algorithm reduces cross-line errors, which improves the
accuracy even further.
