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Audio-to-lyrics alignment has become an increasingly active research task inMIR, supported by the emergence of several open-source datasets of audiorecordings with word-level lyrics annotations. However, there are still anumber of open problems, such as a lack of robustness in the face of severeduration mismatches between audio and lyrics representation; a certain degreeof language-specificity caused by acoustic differences across languages; andthe fact that most successful methods in the field are not suited to work inreal-time. Real-time lyrics alignment (tracking) would have many usefulapplications, such as fully automated subtitle display in live concerts andopera. In this work, we describe the first real-time-capable audio-to-lyricsalignment pipeline that is able to robustly track the lyrics of differentlanguages, without additional language information. The proposed modelpredicts, for each audio frame, a probability vector over (European) phonemeclasses, using a very small temporal context, and aligns this vector with aphoneme posteriogram matrix computed beforehand from another recording of thesame 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 ofclassical opera. Finally, robustness to out-of-training languages isdemonstrated in an experiment on Jingju (Beijing opera).

On-Line Audio-to-Lyrics Alignment Based on a Reference Performance