  Although contextualized automatic speech recognition (ASR) systems are
commonly used to improve the recognition of uncommon words, their effectiveness
is hindered by the inherent limitations of speech-text data availability. To
address this challenge, our study proposes to leverage extensive text-only
datasets and contextualize pre-trained ASR models using a straightforward
text-augmentation (TA) technique, all while keeping computational costs
minimal. In particular, to contextualize a pre-trained CIF-based ASR, we
construct a codebook using limited speech-text data. By utilizing a simple
codebook lookup process, we convert available text-only data into latent text
embeddings. These embeddings then enhance the inputs for the contextualized
ASR. Our experiments on diverse Mandarin test sets demonstrate that our TA
approach significantly boosts recognition performance. The top-performing
system shows relative CER improvements of up to 30% on rare words and 15%
across all words in general.
