15,960 research outputs found
Very Deep Convolutional Neural Networks for Robust Speech Recognition
This paper describes the extension and optimization of our previous work on
very deep convolutional neural networks (CNNs) for effective recognition of
noisy speech in the Aurora 4 task. The appropriate number of convolutional
layers, the sizes of the filters, pooling operations and input feature maps are
all modified: the filter and pooling sizes are reduced and dimensions of input
feature maps are extended to allow adding more convolutional layers.
Furthermore appropriate input padding and input feature map selection
strategies are developed. In addition, an adaptation framework using joint
training of very deep CNN with auxiliary features i-vector and fMLLR features
is developed. These modifications give substantial word error rate reductions
over the standard CNN used as baseline. Finally the very deep CNN is combined
with an LSTM-RNN acoustic model and it is shown that state-level weighted log
likelihood score combination in a joint acoustic model decoding scheme is very
effective. On the Aurora 4 task, the very deep CNN achieves a WER of 8.81%,
further 7.99% with auxiliary feature joint training, and 7.09% with LSTM-RNN
joint decoding.Comment: accepted by SLT 201
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
A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition
Convolutional neural networks are sensitive to unknown noisy condition in the
test phase and so their performance degrades for the noisy data classification
task including noisy speech recognition. In this research, a new convolutional
neural network (CNN) model with data uncertainty handling; referred as NCNN
(Neutrosophic Convolutional Neural Network); is proposed for classification
task. Here, speech signals are used as input data and their noise is modeled as
uncertainty. In this task, using speech spectrogram, a definition of
uncertainty is proposed in neutrosophic (NS) domain. Uncertainty is computed
for each Time-frequency point of speech spectrogram as like a pixel. Therefore,
uncertainty matrix with the same size of spectrogram is created in NS domain.
In the next step, a two parallel paths CNN classification model is proposed.
Speech spectrogram is used as input of the first path and uncertainty matrix
for the second path. The outputs of two paths are combined to compute the final
output of the classifier. To show the effectiveness of the proposed method, it
has been compared with conventional CNN on the isolated words of Aurora2
dataset. The proposed method achieves the average accuracy of 85.96 in noisy
train data. It is more robust against Car, Airport and Subway noises with
accuracies 90, 88 and 81 in test sets A, B and C, respectively. Results show
that the proposed method outperforms conventional CNN with the improvement of
6, 5 and 2 percentage in test set A, test set B and test sets C, respectively.
It means that the proposed method is more robust against noisy data and handle
these data effectively.Comment: International conference on Pattern Recognition and Image Analysis
(IPRIA 2019
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