774 research outputs found
Deep Maxout Networks applied to Noise-Robust Speech Recognition
Proceedings of: IberSPEECH 2014 "VIII Jornadas en Tecnologías del Habla" and "IV Iberian SLTech Workshop". Las Palmas de Gran Canaria, Spain, November 19-21, 2014.Deep Neural Networks (DNN) have become very popular for acoustic modeling due to the improvements found over traditional Gaussian Mixture Models (GMM). However, not many works have addressed the robustness of these systems under noisy conditions. Recently, the machine learning community has proposed new methods to improve the accuracy of DNNs by using techniques such as dropout and maxout. In this paper, we investigate Deep Maxout Networks (DMN) for acoustic modeling in a noisy automatic speech recognition environment. Experiments show that DMNs improve substantially the recognition accuracy over DNNs and other traditional techniques in both clean and noisy conditions on the TIMIT dataset.This contribution has been supported by an Airbus Defense and Space Grant (Open Innovation - SAVIER) and Spanish Government-CICYT project 2011-26807/TEC.Publicad
Less-forgetful Learning for Domain Expansion in Deep Neural Networks
Expanding the domain that deep neural network has already learned without
accessing old domain data is a challenging task because deep neural networks
forget previously learned information when learning new data from a new domain.
In this paper, we propose a less-forgetful learning method for the domain
expansion scenario. While existing domain adaptation techniques solely focused
on adapting to new domains, the proposed technique focuses on working well with
both old and new domains without needing to know whether the input is from the
old or new domain. First, we present two naive approaches which will be
problematic, then we provide a new method using two proposed properties for
less-forgetful learning. Finally, we prove the effectiveness of our method
through experiments on image classification tasks. All datasets used in the
paper, will be released on our website for someone's follow-up study.Comment: 8 pages, accepted to AAAI 201
End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models
Speech activity detection (SAD) plays an important role in current speech
processing systems, including automatic speech recognition (ASR). SAD is
particularly difficult in environments with acoustic noise. A practical
solution is to incorporate visual information, increasing the robustness of the
SAD approach. An audiovisual system has the advantage of being robust to
different speech modes (e.g., whisper speech) or background noise. Recent
advances in audiovisual speech processing using deep learning have opened
opportunities to capture in a principled way the temporal relationships between
acoustic and visual features. This study explores this idea proposing a
\emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach
models the temporal dynamic of the sequential audiovisual data, improving the
accuracy and robustness of the proposed SAD system. Instead of estimating
hand-crafted features, the study investigates an end-to-end training approach,
where acoustic and visual features are directly learned from the raw data
during training. The experimental evaluation considers a large audiovisual
corpus with over 60.8 hours of recordings, collected from 105 speakers. The
results demonstrate that the proposed framework leads to absolute improvements
up to 1.2% under practical scenarios over a VAD baseline using only audio
implemented with deep neural network (DNN). The proposed approach achieves
92.7% F1-score when it is evaluated using the sensors from a portable tablet
under noisy acoustic environment, which is only 1.0% lower than the performance
obtained under ideal conditions (e.g., clean speech obtained with a high
definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio
Reading Scene Text in Deep Convolutional Sequences
We develop a Deep-Text Recurrent Network (DTRN) that regards scene text
reading as a sequence labelling problem. We leverage recent advances of deep
convolutional neural networks to generate an ordered high-level sequence from a
whole word image, avoiding the difficult character segmentation problem. Then a
deep recurrent model, building on long short-term memory (LSTM), is developed
to robustly recognize the generated CNN sequences, departing from most existing
approaches recognising each character independently. Our model has a number of
appealing properties in comparison to existing scene text recognition methods:
(i) It can recognise highly ambiguous words by leveraging meaningful context
information, allowing it to work reliably without either pre- or
post-processing; (ii) the deep CNN feature is robust to various image
distortions; (iii) it retains the explicit order information in word image,
which is essential to discriminate word strings; (iv) the model does not depend
on pre-defined dictionary, and it can process unknown words and arbitrary
strings. Codes for the DTRN will be available.Comment: To appear in the 13th AAAI Conference on Artificial Intelligence
(AAAI-16), 201
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