  This paper introduces ASTRA, a novel method for improving Automatic Speech
Recognition (ASR) through text injection.Unlike prevailing techniques, ASTRA
eliminates the need for sampling to match sequence lengths between speech and
text modalities. Instead, it leverages the inherent alignments learned within
CTC/RNNT models. This approach offers the following two advantages, namely,
avoiding potential misalignment between speech and text features that could
arise from upsampling and eliminating the need for models to accurately predict
duration of sub-word tokens. This novel formulation of modality (length)
matching as a weighted RNNT objective matches the performance of the
state-of-the-art duration-based methods on the FLEURS benchmark, while opening
up other avenues of research in speech processing.
