Document Type

Conference Paper


Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence



Publication Details

6th International Workshop on Folk Music Analysis, 15-17, June, 2016.


In this study we propose how to modify a standard approach for text-to-speech alignment to apply in the case of alignment of lyrics and singing voice. We model phoneme durations by means of a duration-explicit hidden Markov model (DHMM) phonetic recognizer based on MFCCs. The phoneme durations are empirically set in a probabilistic way, based on prior knowledge about the lyrics structure and metric principles, specific for the Beijing opera music tradition. Phoneme models are GMMs trained directly on a small corpus of annotated singing voice. The alignment is evaluated on a cappella material from Beijing opera, which is characterized by its particularly long syllable durations. Results show that the incorporation of music-specific knowledge results in a very high alignment accuracy, outperforming significantly a baseline HMM-based approach.


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