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- W4306703742 abstract "We present JOIST, an algorithm to train a streaming, cascaded, encoder end-to-end (E2E) model with both speech-text paired inputs, and text-only unpaired inputs. Unlike previous works, we explore joint training with both modalities, rather than pre-training and fine-tuning. In addition, we explore JOIST using a streaming E2E model with an order of magnitude more data, which are also novelties compared to previous works. Through a series of ablation studies, we explore different types of text modeling, including how to model the length of the text sequence and the appropriate text sub-word unit representation. We find that best text representation for JOIST improves WER across a variety of search and rare-word test sets by 4-14% relative, compared to a model not trained with text. In addition, we quantitatively show that JOIST maintains streaming capabilities, which is important for good user-level experience." @default.
- W4306703742 created "2022-10-19" @default.
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- W4306703742 date "2022-10-13" @default.
- W4306703742 modified "2023-10-16" @default.
- W4306703742 title "JOIST: A Joint Speech and Text Streaming Model For ASR" @default.
- W4306703742 doi "https://doi.org/10.48550/arxiv.2210.07353" @default.
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