Autoregressive Co-Training for Learning Discrete Speech Representations

• URL: http://arxiv.org/abs/2203.15840v1
• Date: Tue, 29 Mar 2022 18:17:18 GMT
• Title: Autoregressive Co-Training for Learning Discrete Speech Representations
• Authors: Sung-Lin Yeh, Hao Tang
• Abstract summary: We consider a generative model with discrete latent variables that learns a discrete representation for speech. We find that the proposed approach learns discrete representation that is highly correlated with phonetic units.
• Score: 19.400428010647573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While several self-supervised approaches for learning discrete speech representation have been proposed, it is unclear how these seemingly similar approaches relate to each other. In this paper, we consider a generative model with discrete latent variables that learns a discrete representation for speech. The objective of learning the generative model is formulated as information-theoretic co-training. Besides the wide generality, the objective can be optimized with several approaches, subsuming HuBERT-like training and vector quantization for learning discrete representation. Empirically, we find that the proposed approach learns discrete representation that is highly correlated with phonetic units, more correlated than HuBERT-like training and vector quantization.

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