  Human-computer interaction (HCI) is significantly impacted by delayed
responses from a spoken dialogue system. Hence, end-to-end (e2e) spoken
language understanding (SLU) solutions have recently been proposed to decrease
latency. Such approaches allow for the extraction of semantic information
directly from the speech signal, thus bypassing the need for a transcript from
an automatic speech recognition (ASR) system. In this paper, we propose a
compact e2e SLU architecture for streaming scenarios, where chunks of the
speech signal are processed continuously to predict intent and slot values. Our
model is based on a 3D convolutional neural network (3D-CNN) and a
unidirectional long short-term memory (LSTM). We compare the performance of two
alignment-free losses: the connectionist temporal classification (CTC) method
and its adapted version, namely connectionist temporal localization (CTL). The
latter performs not only the classification but also localization of sequential
audio events. The proposed solution is evaluated on the Fluent Speech Command
dataset and results show our model ability to process incoming speech signal,
reaching accuracy as high as 98.97 % for CTC and 98.78 % for CTL on
single-label classification, and as high as 95.69 % for CTC and 95.28 % for CTL
on two-label prediction.
