  This study addresses unsupervised subword modeling, i.e., learning acoustic
feature representations that can distinguish between subword units of a
language. We propose a two-stage learning framework that combines
self-supervised learning and cross-lingual knowledge transfer. The framework
consists of autoregressive predictive coding (APC) as the front-end and a
cross-lingual deep neural network (DNN) as the back-end. Experiments on the ABX
subword discriminability task conducted with the Libri-light and ZeroSpeech
2017 databases showed that our approach is competitive or superior to
state-of-the-art studies. Comprehensive and systematic analyses at the phoneme-
and articulatory feature (AF)-level showed that our approach was better at
capturing diphthong than monophthong vowel information, while also differences
in the amount of information captured for different types of consonants were
observed. Moreover, a positive correlation was found between the effectiveness
of the back-end in capturing a phoneme's information and the quality of the
cross-lingual phone labels assigned to the phoneme. The AF-level analysis
together with t-SNE visualization results showed that the proposed approach is
better than MFCC and APC features in capturing manner and place of articulation
information, vowel height, and backness information. Taken together, the
analyses showed that the two stages in our approach are both effective in
capturing phoneme and AF information. Nevertheless, monophthong vowel
information is less well captured than consonant information, which suggests
that future research should focus on improving capturing monophthong vowel
information.
