  The goal of real-time lyrics alignment is to take live singing audio as input
and to pinpoint the exact position within given lyrics on the fly. The task can
benefit real-world applications such as the automatic subtitling of live
concerts or operas. However, designing a real-time model poses a great
challenge due to the constraints of only using past input and operating within
a minimal latency. Furthermore, due to the lack of datasets for real-time
models for lyrics alignment, previous studies have mostly evaluated with
private in-house datasets, resulting in a lack of standard evaluation methods.
This paper presents a real-time lyrics alignment system for classical vocal
performances with two contributions. First, we improve the lyrics alignment
algorithm by finding an optimal combination of chromagram and phonetic
posteriorgram (PPG) that capture melodic and phonetics features of the singing
voice, respectively. Second, we recast the Schubert Winterreise Dataset (SWD)
which contains multiple performance renditions of the same pieces as an
evaluation set for the real-time lyrics alignment.
