Research on speech processing has traditionally considered the task of
designing hand-engineered acoustic features (feature engineering) as a separate
distinct problem from the task of designing efficient machine learning (ML)
models to make prediction and classification decisions. There are two main
drawbacks to this approach: firstly, the feature engineering being manual is
cumbersome and requires human knowledge; and secondly, the designed features
might not be best for the objective at hand. This has motivated the adoption of
a recent trend in speech community towards utilisation of representation
learning techniques, which can learn an intermediate representation of the
input signal automatically that better suits the task at hand and hence lead to
improved performance. The significance of representation learning has increased
with advances in deep learning (DL), where the representations are more useful
and less dependent on human knowledge, making it very conducive for tasks like
classification, prediction, etc. The main contribution of this paper is to
present an up-to-date and comprehensive survey on different techniques of
speech representation learning by bringing together the scattered research
across three distinct research areas including Automatic Speech Recognition
(ASR), Speaker Recognition (SR), and Speaker Emotion Recognition (SER). Recent
reviews in speech have been conducted for ASR, SR, and SER, however, none of
these has focused on the representation learning from speech -- a gap that our
survey aims to bridge.

本文介绍了语音表征学习的不同技术的最新综述，将分散的研究汇集到了自动语音识别、说话人识别和说话人情感识别三个研究领域中，并弥补了以前综述中没有涉及语音表征学习的空白。