  Audio-to-lyrics alignment has become an increasingly active research task in
MIR, supported by the emergence of several open-source datasets of audio
recordings with word-level lyrics annotations. However, there are still a
number of open problems, such as a lack of robustness in the face of severe
duration mismatches between audio and lyrics representation; a certain degree
of language-specificity caused by acoustic differences across languages; and
the fact that most successful methods in the field are not suited to work in
real-time. Real-time lyrics alignment (tracking) would have many useful
applications, such as fully automated subtitle display in live concerts and
opera. In this work, we describe the first real-time-capable audio-to-lyrics
alignment pipeline that is able to robustly track the lyrics of different
languages, without additional language information. The proposed model
predicts, for each audio frame, a probability vector over (European) phoneme
classes, using a very small temporal context, and aligns this vector with a
phoneme posteriogram matrix computed beforehand from another recording of the
same work, which serves as a reference and a proxy to the written-out lyrics.
We evaluate our system's tracking accuracy on the challenging genre of
classical opera. Finally, robustness to out-of-training languages is
demonstrated in an experiment on Jingju (Beijing opera).
