Unsupervised Subword Modeling Using Autoregressive Pretraining and Cross-Lingual Phone-Aware Modeling
Table 3: ABX error rates of BNFs on ZeroSpeech 2017 English evaluation sets. Models are trained with Libri-light.
['System', 'Hours', 'Across-speaker 1s', 'Across-speaker 10s', 'Across-speaker 120s', 'Across-speaker Avg.', 'Within-speaker 1s', 'Within-speaker 10s', 'Within-speaker 120s', 'Within-speaker Avg.']
We also evaluated the performance of our approach on the ZeroSpeech 2017 English evaluation sets. Note that, unlike our approach, these two systems employed English labeled data. The total amount of labeled training data used in [shibata2017composite] is 1,327 hours (including 80-hour English data). In this table, “Proposed-Du/-Ma” denotes our proposed approach by adopting Dutch or Mandarin labels respectively. Interestingly, using Dutch labels, our system trained with 526 hours of data outperforms the topline and [shibata2017composite] systems, and is comparable to the two reference systems when trained with only 104 hours of data.