  The Sparsespeech model is an unsupervised acoustic model that can generate
discrete pseudo-labels for untranscribed speech. We extend the Sparsespeech
model to allow for sampling over a random discrete variable, yielding
pseudo-posteriorgrams. The degree of sparsity in this posteriorgram can be
fully controlled after the model has been trained. We use the Gumbel-Softmax
trick to approximately sample from a discrete distribution in the neural
network and this allows us to train the network efficiently with standard
backpropagation. The new and improved model is trained and evaluated on the
Libri-Light corpus, a benchmark for ASR with limited or no supervision. The
model is trained on 600h and 6000h of English read speech. We evaluate the
improved model using the ABX error measure and a semi-supervised setting with
10h of transcribed speech. We observe a relative improvement of up to 31.4% on
ABX error rates across speakers on the test set with the improved Sparsespeech
model on 600h of speech data and further improvements when we scale the model
to 6000h.
