Improving Unsupervised Sparsespeech Acoustic Models with Categorical Reparameterization
| Autor: | Benjamin Milde, Chris Biemann |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Scale (ratio) Artificial neural network Computer science Speech recognition Acoustic model computer.file_format Backpropagation Audio and Speech Processing (eess.AS) Test set FOS: Electrical engineering electronic engineering information engineering Probability distribution ABX test Computation and Language (cs.CL) Categorical variable computer Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | INTERSPEECH |
| DOI: | 10.21437/interspeech.2020-2629 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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