  We present a comprehensive study on building and adapting RNN transducer
(RNN-T) models for spoken language understanding(SLU). These end-to-end (E2E)
models are constructed in three practical settings: a case where verbatim
transcripts are available, a constrained case where the only available
annotations are SLU labels and their values, and a more restrictive case where
transcripts are available but not corresponding audio. We show how RNN-T SLU
models can be developed starting from pre-trained automatic speech recognition
(ASR) systems, followed by an SLU adaptation step. In settings where real audio
data is not available, artificially synthesized speech is used to successfully
adapt various SLU models. When evaluated on two SLU data sets, the ATIS corpus
and a customer call center data set, the proposed models closely track the
performance of other E2E models and achieve state-of-the-art results.
