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
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Abstract
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.