  Often, the storage and computational constraints of embeddeddevices demand
that a single on-device ASR model serve multiple use-cases / domains. In this
paper, we propose aFlexibleTransducer(FlexiT) for on-device automatic speech
recognition to flexibly deal with multiple use-cases / domains with different
accuracy and latency requirements. Specifically, using a single compact model,
FlexiT provides a fast response for voice commands, and accurate transcription
but with more latency for dictation. In order to achieve flexible and better
accuracy and latency trade-offs, the following techniques are used. Firstly, we
propose using domain-specific altering of segment size for Emformer encoder
that enables FlexiT to achieve flexible de-coding. Secondly, we use Alignment
Restricted RNNT loss to achieve flexible fine-grained control on token emission
latency for different domains. Finally, we add a domain indicator vector as an
additional input to the FlexiT model. Using the combination of techniques, we
show that a single model can be used to improve WERs and real time factor for
dictation scenarios while maintaining optimal latency for voice commands
use-cases
