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We present JOIST, an algorithm to train a streaming, cascaded, encoderend-to-end (E2E) model with both speech-text paired inputs, and text-onlyunpaired inputs. Unlike previous works, we explore joint training with bothmodalities, rather than pre-training and fine-tuning. In addition, we exploreJOIST using a streaming E2E model with an order of magnitude more data, whichare also novelties compared to previous works. Through a series of ablationstudies, we explore different types of text modeling, including how to modelthe length of the text sequence and the appropriate text sub-word unitrepresentation. We find that best text representation for JOIST improves WERacross a variety of search and rare-word test sets by 4-14% relative, comparedto a model not trained with text. In addition, we quantitatively show thatJOIST maintains streaming capabilities, which is important for good user-levelexperience.

JOIST: A Joint Speech and Text Streaming Model For ASR