1 research outputs found
Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data
Most state-of-the-art speech systems are using Deep Neural Networks (DNNs).
Those systems require a large amount of data to be learned. Hence, learning
state-of-the-art frameworks on under-resourced speech languages/problems is a
difficult task. Problems could be the limited amount of data for impaired
speech. Furthermore, acquiring more data and/or expertise is time-consuming and
expensive. In this paper we position ourselves for the following speech
processing tasks: Automatic Speech Recognition, speaker identification and
emotion recognition. To assess the problem of limited data, we firstly
investigate state-of-the-art Automatic Speech Recognition systems as it
represents the hardest tasks (due to the large variability in each language).
Next, we provide an overview of techniques and tasks requiring fewer data. In
the last section we investigate few-shot techniques as we interpret
under-resourced speech as a few-shot problem. In that sense we propose an
overview of few-shot techniques and perspectives of using such techniques for
the focused speech problems in this survey. It occurs that the reviewed
techniques are not well adapted for large datasets. Nevertheless, some
promising results from the literature encourage the usage of such techniques
for speech processing