279,319 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
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
Information Loss in the Human Auditory System
From the eardrum to the auditory cortex, where acoustic stimuli are decoded,
there are several stages of auditory processing and transmission where
information may potentially get lost. In this paper, we aim at quantifying the
information loss in the human auditory system by using information theoretic
tools.
To do so, we consider a speech communication model, where words are uttered
and sent through a noisy channel, and then received and processed by a human
listener.
We define a notion of information loss that is related to the human word
recognition rate. To assess the word recognition rate of humans, we conduct a
closed-vocabulary intelligibility test. We derive upper and lower bounds on the
information loss. Simulations reveal that the bounds are tight and we observe
that the information loss in the human auditory system increases as the signal
to noise ratio (SNR) decreases. Our framework also allows us to study whether
humans are optimal in terms of speech perception in a noisy environment.
Towards that end, we derive optimal classifiers and compare the human and
machine performance in terms of information loss and word recognition rate. We
observe a higher information loss and lower word recognition rate for humans
compared to the optimal classifiers. In fact, depending on the SNR, the machine
classifier may outperform humans by as much as 8 dB. This implies that for the
speech-in-stationary-noise setup considered here, the human auditory system is
sub-optimal for recognizing noisy words
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