Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis
Jianqiao Lu,
Wenyong Huang,
Nianzu Zheng
et al.
Abstract:Training a high performance end-to-end speech (E2E) processing model requires an enormous amount of labeled speech data, especially in the era of data-centric artificial intelligence. However, labeled speech data are usually scarcer and more expensive for collection, compared to textual data. We propose Latent Synthesis (LaSyn), an efficient textual data utilization framework for E2E speech processing models. We train a latent synthesizer to convert textual data into an intermediate latent representation of a … Show more
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