  Spoken Language Understanding (SLU) typically comprises of an automatic
speech recognition (ASR) followed by a natural language understanding (NLU)
module. The two modules process signals in a blocking sequential fashion, i.e.,
the NLU often has to wait for the ASR to finish processing on an utterance
basis, potentially leading to high latencies that render the spoken interaction
less natural. In this paper, we propose recurrent neural network (RNN) based
incremental processing towards the SLU task of intent detection. The proposed
methodology offers lower latencies than a typical SLU system, without any
significant reduction in system accuracy. We introduce and analyze different
recurrent neural network architectures for incremental and online processing of
the ASR transcripts and compare it to the existing offline systems. A lexical
End-of-Sentence (EOS) detector is proposed for segmenting the stream of
transcript into sentences for intent classification. Intent detection
experiments are conducted on benchmark ATIS, Snips and Facebook's multilingual
task oriented dialog datasets modified to emulate a continuous incremental
stream of words with no utterance demarcation. We also analyze the prospects of
early intent detection, before EOS, with our proposed system.
