  Neural transducer-based systems such as RNN Transducers (RNN-T) for automatic
speech recognition (ASR) blend the individual components of a traditional
hybrid ASR systems (acoustic model, language model, punctuation model, inverse
text normalization) into one single model. This greatly simplifies training and
inference and hence makes RNN-T a desirable choice for ASR systems. In this
work, we investigate use of RNN-T in applications that require a tune-able
latency budget during inference time. We also improved the decoding speed of
the originally proposed RNN-T beam search algorithm. We evaluated our proposed
system on English videos ASR dataset and show that neural RNN-T models can
achieve comparable WER and better computational efficiency compared to a well
tuned hybrid ASR baseline.
