  An intelligent virtual assistant (IVA) enables effortless conversations in
call routing through spoken utterance classification (SUC) which is a special
form of spoken language understanding (SLU). Building a SUC system requires a
large amount of supervised in-domain data that is not always available. In this
paper, we introduce an unsupervised spoken utterance classification approach
(USUC) that does not require any in-domain data except for the intent labels
and a few para-phrases per intent. USUC is consisting of a KNN classifier (K=1)
and a complex embedding model trained on a large amount of unsupervised
customer service corpus. Among all embedding models, we demonstrate that Elmo
works best for USUC. However, an Elmo model is too slow to be used at run-time
for call routing. To resolve this issue, first, we compute the uni- and bi-gram
embedding vectors offline and we build a lookup table of n-grams and their
corresponding embedding vector. Then we use this table to compute sentence
embedding vectors at run-time, along with back-off techniques for unseen
n-grams. Experiments show that USUC outperforms the traditional utterance
classification methods by reducing the classification error rate from 32.9% to
27.0% without requiring supervised data. Moreover, our lookup and back-off
technique increases the processing speed from 16 utterances per second to 118
utterances per second.
