Research on the Recognition and Application of Montreal Forced Aligner for Singing Audio

Jinyu Liu

doi:10.54097/ohpdubg1

Authors

Jinyu Liu

DOI:

https://doi.org/10.54097/ohpdubg1

Keywords:

MFA, Singing audio, Phoneme alignment, Model training

Abstract

This paper discusses the feasibility of obtaining phoneme-aligned time segments for singing audio using the Montreal Forced Aligner (MFA) tool. Initially, the recognition effectiveness of singing audio data is tested using the open-source MFA model. Subsequently, samples with high recognition accuracy are manually annotated and used to train the MFA model. Finally, the recognition effectiveness of the open-source MFA model and the MFA model trained on singing audio data is observed. It is found that the performance of the trained MFA model is significantly improved. This also confirms the feasibility of MFA in recognizing singing audio.

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