Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings
Table 1: Comparison of different methods on training and testing data.
['[BOLD] Methods', '[BOLD] Methods', '[BOLD] Methods', '[BOLD] Accuracy  [BOLD] Train', '[BOLD] Accuracy  [BOLD] Test']
Random baseline (row(a)) guessed uniformly from the 39 phoneme, while the most frequent phoneme baseline (row (b)) simply guessed the most frequent phoneme,/ix/,for all segments. using softmax and gumbel-softmax It is clear that even with unrelated text the proposed approach significantly outperformed the trivial baselines (rows (e), (f) v.s. (a), (b)). For unrelated data, we also integrated the output of 6 models with slightly different parameters in the generator and discriminator by majority vote for both softmax and gumbel-softmax (rows (g), (h)). Obviously this improved the performance (rows (g) v.s. (e), (h) v.s. (f)), giving an accuracy exceeding 36%. In all cases the training and testing results were very close. For unsupervised learning of phoneme characteristics, whether the model has seen the data is not important.