  Unsupervised discovery of acoustic tokens from audio corpora without
annotation and learning vector representations for these tokens have been
widely studied. Although these techniques have been shown successful in some
applications such as query-by-example Spoken Term Detection (STD), the lack of
mapping relationships between these discovered tokens and real phonemes have
limited the down-stream applications. This paper represents probably the first
attempt towards the goal of completely unsupervised phoneme recognition, or
mapping audio signals to phoneme sequences without phoneme-labeled audio data.
The basic idea is to cluster the embedded acoustic tokens and learn the mapping
between the cluster sequences and the unknown phoneme sequences with a
Generative Adversarial Network (GAN). An unsupervised phoneme recognition
accuracy of 36% was achieved in the preliminary experiments.
