Spoken dialog systems are slowly becoming and integral part of the human
experience due to their various advantages over textual interfaces. Spoken
language understanding (SLU) systems are fundamental building blocks of spoken
dialog systems. But creating SLU systems for low resourced languages is still a
challenge. In a large number of low resourced language, we don't have access to
enough data to build automatic speech recognition (ASR) technologies, which are
fundamental to any SLU system. Also, ASR based SLU systems do not generalize to
unwritten languages. In this paper, we present a series of experiments to
explore extremely low-resourced settings where we perform intent classification
with systems trained on as low as one data-point per intent and with only one
speaker in the dataset. We also work in a low-resourced setting where we do not
use language specific ASR systems to transcribe input speech, which compounds
the challenge of building SLU systems to simulate a true low-resourced setting.
We test our system on Belgian Dutch (Flemish) and English and find that using
phonetic transcriptions to make intent classification systems in such
low-resourced setting performs significantly better than using speech features.
Specifically, when using a phonetic transcription based system over a feature
based system, we see average improvements of 12.37% and 13.08% for binary and
four-class classification problems respectively, when averaged over 49
different experimental settings.

研究探索低资源语言环境下，使用拼音转录进行意图分类的方法构建基于语音理解系统，并发现相较于使用语音特征的系统，使用拼音转录的系统具有显著的分类性能提升。