Evaluasi Montreal Forced Aligner dan Goodness of Pronunciation untuk Penilaian Pelafalan Bahasa Sunda - Abdul Fatahillah; Sigit Puspito Wigati Jarot (TIN: Terapan Informatika Nusantara, 2026)
Sundanese is the second most widely spoken regional language in Indonesia, yet automated pronunciation assessment systems for this language remain extremely scarce. This study presents a systematic evaluation of the Montreal Forced Aligner (MFA) and Goodness of Pronunciation (GOP) pipeline for Sundanese pronunciation assessment within a prototype voice-based learning application. The dataset comprises 2,500 valid utterance samples collected from 50 native Sundanese speakers, covering 10 basa loma vocabulary items spanning 20 unique phonemes. MFA evaluation revealed total and systemic alignment failure: all 2,500 files (100%) were identified as problematic, with 17 of 20 phonemes consistently assigned exactly 10-millisecond durations. Three distinct parameter configurations produced identical failure rates (100%), confirming that the failures are intrinsic to MFA's limitations with very short-duration single-word audio (mean 0.69 seconds) for low-resource languages. GOP evaluation yielded a global top-1 accuracy of only 26.1%, characterized by anomalous dominance of the /l/ phoneme as top-1 for 14 of 20 phonemes. Functional testing demonstrated the system's inability to discriminate correct from incorrect utterances. On the technical side, the React Native and FastAPI prototype application was successfully implemented, with 6 of 8 black-box test scenarios passing. This research provides three principal contributions: (1) empirical contribution in the form of the first quantitative evidence that the standard MFA-GOP pipeline cannot be directly applied to Sundanese as a low-resource language with short-duration single-word audio; (2) methodological contribution in the form of an empirical baseline and replicable evaluation framework applicable to other regional languages of Indonesia; and (3) practical contribution in the form of a React Native–FastAPI client-server prototype that serves as a starting point for further development of Sundanese pronunciation assessment systems using alternative approaches.