72 research outputs found
Single-Microphone Speech Enhancement and Separation Using Deep Learning
The cocktail party problem comprises the challenging task of understanding a
speech signal in a complex acoustic environment, where multiple speakers and
background noise signals simultaneously interfere with the speech signal of
interest. A signal processing algorithm that can effectively increase the
speech intelligibility and quality of speech signals in such complicated
acoustic situations is highly desirable. Especially for applications involving
mobile communication devices and hearing assistive devices. Due to the
re-emergence of machine learning techniques, today, known as deep learning, the
challenges involved with such algorithms might be overcome. In this PhD thesis,
we study and develop deep learning-based techniques for two sub-disciplines of
the cocktail party problem: single-microphone speech enhancement and
single-microphone multi-talker speech separation. Specifically, we conduct
in-depth empirical analysis of the generalizability capability of modern deep
learning-based single-microphone speech enhancement algorithms. We show that
performance of such algorithms is closely linked to the training data, and good
generalizability can be achieved with carefully designed training data.
Furthermore, we propose uPIT, a deep learning-based algorithm for
single-microphone speech separation and we report state-of-the-art results on a
speaker-independent multi-talker speech separation task. Additionally, we show
that uPIT works well for joint speech separation and enhancement without
explicit prior knowledge about the noise type or number of speakers. Finally,
we show that deep learning-based speech enhancement algorithms designed to
minimize the classical short-time spectral amplitude mean squared error leads
to enhanced speech signals which are essentially optimal in terms of STOI, a
state-of-the-art speech intelligibility estimator.Comment: PhD Thesis. 233 page
Deep neural network techniques for monaural speech enhancement: state of the art analysis
Deep neural networks (DNN) techniques have become pervasive in domains such
as natural language processing and computer vision. They have achieved great
success in these domains in task such as machine translation and image
generation. Due to their success, these data driven techniques have been
applied in audio domain. More specifically, DNN models have been applied in
speech enhancement domain to achieve denosing, dereverberation and
multi-speaker separation in monaural speech enhancement. In this paper, we
review some dominant DNN techniques being employed to achieve speech
separation. The review looks at the whole pipeline of speech enhancement from
feature extraction, how DNN based tools are modelling both global and local
features of speech and model training (supervised and unsupervised). We also
review the use of speech-enhancement pre-trained models to boost speech
enhancement process. The review is geared towards covering the dominant trends
with regards to DNN application in speech enhancement in speech obtained via a
single speaker.Comment: conferenc
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