43,547 research outputs found
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition
This article provides a unifying Bayesian network view on various approaches
for acoustic model adaptation, missing feature, and uncertainty decoding that
are well-known in the literature of robust automatic speech recognition. The
representatives of these classes can often be deduced from a Bayesian network
that extends the conventional hidden Markov models used in speech recognition.
These extensions, in turn, can in many cases be motivated from an underlying
observation model that relates clean and distorted feature vectors. By
converting the observation models into a Bayesian network representation, we
formulate the corresponding compensation rules leading to a unified view on
known derivations as well as to new formulations for certain approaches. The
generic Bayesian perspective provided in this contribution thus highlights
structural differences and similarities between the analyzed approaches
DNN-Based Multi-Frame MVDR Filtering for Single-Microphone Speech Enhancement
Multi-frame approaches for single-microphone speech enhancement, e.g., the
multi-frame minimum-variance-distortionless-response (MVDR) filter, are able to
exploit speech correlations across neighboring time frames. In contrast to
single-frame approaches such as the Wiener gain, it has been shown that
multi-frame approaches achieve a substantial noise reduction with hardly any
speech distortion, provided that an accurate estimate of the correlation
matrices and especially the speech interframe correlation vector is available.
Typical estimation procedures of the correlation matrices and the speech
interframe correlation (IFC) vector require an estimate of the speech presence
probability (SPP) in each time-frequency bin. In this paper, we propose to use
a bi-directional long short-term memory deep neural network (DNN) to estimate a
speech mask and a noise mask for each time-frequency bin, using which two
different SPP estimates are derived. Aiming at achieving a robust performance,
the DNN is trained for various noise types and signal-to-noise ratios.
Experimental results show that the multi-frame MVDR in combination with the
proposed data-driven SPP estimator yields an increased speech quality compared
to a state-of-the-art model-based estimator
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
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