  End-to-end multilingual speech recognition models handle multiple languages
through a single model, often incorporating language identification to
automatically detect the language of incoming speech. Since the common scenario
is where the language is already known, these models can perform as
language-specific by using language information as prompts, which is
particularly beneficial for attention-based encoder-decoder architectures.
However, the Connectionist Temporal Classification (CTC) approach, which
enhances recognition via joint decoding and multi-task training, does not
normally incorporate language prompts due to its conditionally independent
output tokens. To overcome this, we introduce an encoder prompting technique
within the self-conditioned CTC framework, enabling language-specific
adaptation of the CTC model in a zero-shot manner. Our method has shown to
significantly reduce errors by 28% on average and by 41% on low-resource
languages.
