4,878 research outputs found
Phonetic Temporal Neural Model for Language Identification
Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We present a phonetic temporal neural model for LID,
which is an LSTM-RNN LID system that accepts phonetic features produced by a
phone-discriminative DNN as the input, rather than raw acoustic features. This
new model is similar to traditional phonetic LID methods, but the phonetic
knowledge here is much richer: it is at the frame level and involves compacted
information of all phones. Our experiments conducted on the Babel database and
the AP16-OLR database demonstrate that the temporal phonetic neural approach is
very effective, and significantly outperforms existing acoustic neural models.
It also outperforms the conventional i-vector approach on short utterances and
in noisy conditions.Comment: Submitted to TASL
Zero-shot keyword spotting for visual speech recognition in-the-wild
Visual keyword spotting (KWS) is the problem of estimating whether a text
query occurs in a given recording using only video information. This paper
focuses on visual KWS for words unseen during training, a real-world, practical
setting which so far has received no attention by the community. To this end,
we devise an end-to-end architecture comprising (a) a state-of-the-art visual
feature extractor based on spatiotemporal Residual Networks, (b) a
grapheme-to-phoneme model based on sequence-to-sequence neural networks, and
(c) a stack of recurrent neural networks which learn how to correlate visual
features with the keyword representation. Different to prior works on KWS,
which try to learn word representations merely from sequences of graphemes
(i.e. letters), we propose the use of a grapheme-to-phoneme encoder-decoder
model which learns how to map words to their pronunciation. We demonstrate that
our system obtains very promising visual-only KWS results on the challenging
LRS2 database, for keywords unseen during training. We also show that our
system outperforms a baseline which addresses KWS via automatic speech
recognition (ASR), while it drastically improves over other recently proposed
ASR-free KWS methods.Comment: Accepted at ECCV-201
Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates
This paper presents a novel approach for indoor acoustic source localization
using microphone arrays and based on a Convolutional Neural Network (CNN). The
proposed solution is, to the best of our knowledge, the first published work in
which the CNN is designed to directly estimate the three dimensional position
of an acoustic source, using the raw audio signal as the input information
avoiding the use of hand crafted audio features. Given the limited amount of
available localization data, we propose in this paper a training strategy based
on two steps. We first train our network using semi-synthetic data, generated
from close talk speech recordings, and where we simulate the time delays and
distortion suffered in the signal that propagates from the source to the array
of microphones. We then fine tune this network using a small amount of real
data. Our experimental results show that this strategy is able to produce
networks that significantly improve existing localization methods based on
\textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN
method exhibits better resistance against varying gender of the speaker and
different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table
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