Creating Spoken Dialog Systems in Ultra-Low Resourced Settings
Moayad Elamin Muhammad Omer Yonas Chanie Henslaac Ndlovu
Automatic Speech Recognition (ASR) systems are a crucial technology that is
used today to design a wide variety of applications, most notably, smart
assistants, such as Alexa. ASR systems are essentially dialogue systems that
employ Spoken Language Understanding (SLU) to extract meaningful information
from speech. The main challenge with designing such systems is that they
require a huge amount of labeled clean data to perform competitively, such data
is extremely hard to collect and annotate to respective SLU tasks, furthermore,
when designing such systems for low resource languages, where data is extremely
limited, the severity of the problem intensifies. In this paper, we focus on a
fairly popular SLU task, that is, Intent Classification while working with a
low resource language, namely, Flemish. Intent Classification is a task
concerned with understanding the intents of the user interacting with the
system. We build on existing light models for intent classification in Flemish,
and our main contribution is applying different augmentation techniques on two
levels -- the voice level, and the phonetic transcripts level -- to the
existing models to counter the problem of scarce labeled data in low-resource
languages. We find that our data augmentation techniques, on both levels, have
improved the model performance on a number of tasks.