117,807 research outputs found
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
Improving Source Separation via Multi-Speaker Representations
Lately there have been novel developments in deep learning towards solving
the cocktail party problem. Initial results are very promising and allow for
more research in the domain. One technique that has not yet been explored in
the neural network approach to this task is speaker adaptation. Intuitively,
information on the speakers that we are trying to separate seems fundamentally
important for the speaker separation task. However, retrieving this speaker
information is challenging since the speaker identities are not known a priori
and multiple speakers are simultaneously active. There is thus some sort of
chicken and egg problem. To tackle this, source signals and i-vectors are
estimated alternately. We show that blind multi-speaker adaptation improves the
results of the network and that (in our case) the network is not capable of
adequately retrieving this useful speaker information itself
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