Evaluating Self-supervised Speech Models on a Taiwanese Hokkien Corpus
Yi-Hui Chou Kalvin Chang Meng-Ju Wu Winston Ou Alice Wen-Hsin Bi Carol Yang Bryan Y. Chen Rong-Wei Pai Po-Yen Yeh Jo-Peng Chiang Iu-Tshian Phoann Winnie Chang Chenxuan Cui Noel Chen Jiatong Shi
Taiwanese Hokkien is declining in use and status due to a language shift
towards Mandarin in Taiwan. This is partly why it is a low resource language in
NLP and speech research today. To ensure that the state of the art in speech
processing does not leave Taiwanese Hokkien behind, we contribute a 1.5-hour
dataset of Taiwanese Hokkien to ML-SUPERB's hidden set. Evaluating ML-SUPERB's
suite of self-supervised learning (SSL) speech representations on our dataset,
we find that model size does not consistently determine performance. In fact,
certain smaller models outperform larger ones. Furthermore, linguistic
alignment between pretraining data and the target language plays a crucial
role.