41,159 research outputs found
Visual Speech Enhancement
When video is shot in noisy environment, the voice of a speaker seen in the
video can be enhanced using the visible mouth movements, reducing background
noise. While most existing methods use audio-only inputs, improved performance
is obtained with our visual speech enhancement, based on an audio-visual neural
network. We include in the training data videos to which we added the voice of
the target speaker as background noise. Since the audio input is not sufficient
to separate the voice of a speaker from his own voice, the trained model better
exploits the visual input and generalizes well to different noise types. The
proposed model outperforms prior audio visual methods on two public lipreading
datasets. It is also the first to be demonstrated on a dataset not designed for
lipreading, such as the weekly addresses of Barack Obama.Comment: Accepted to Interspeech 2018. Supplementary video:
https://www.youtube.com/watch?v=nyYarDGpcY
Improvements to deep convolutional neural networks for LVCSR
Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural
Networks (DNN), as they are able to better reduce spectral variation in the
input signal. This has also been confirmed experimentally, with CNNs showing
improvements in word error rate (WER) between 4-12% relative compared to DNNs
across a variety of LVCSR tasks. In this paper, we describe different methods
to further improve CNN performance. First, we conduct a deep analysis comparing
limited weight sharing and full weight sharing with state-of-the-art features.
Second, we apply various pooling strategies that have shown improvements in
computer vision to an LVCSR speech task. Third, we introduce a method to
effectively incorporate speaker adaptation, namely fMLLR, into log-mel
features. Fourth, we introduce an effective strategy to use dropout during
Hessian-free sequence training. We find that with these improvements,
particularly with fMLLR and dropout, we are able to achieve an additional 2-3%
relative improvement in WER on a 50-hour Broadcast News task over our previous
best CNN baseline. On a larger 400-hour BN task, we find an additional 4-5%
relative improvement over our previous best CNN baseline.Comment: 6 pages, 1 figur
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