  SLU combines ASR and NLU capabilities to accomplish speech-to-intent
understanding. In this paper, we compare different ways to combine ASR and NLU,
in particular using a single Conformer model with different ways to use its
components, to better understand the strengths and weaknesses of each approach.
We find that it is not necessarily a choice between two-stage decoding and
end-to-end systems which determines the best system for research or
application. System optimization still entails carefully improving the
performance of each component. It is difficult to prove that one direction is
conclusively better than the other. In this paper, we also propose a novel
connectionist temporal summarization (CTS) method to reduce the length of
acoustic encoding sequences while improving the accuracy and processing speed
of end-to-end models. This method achieves the same intent accuracy as the best
two-stage SLU recognition with complicated and time-consuming decoding but does
so at lower computational cost. This stacked end-to-end SLU system yields an
intent accuracy of 93.97% for the SmartLights far-field set, 95.18% for the
close-field set, and 99.71% for FluentSpeech.

Computation and Language;Sound;Audio and Speech Processing