1,142 research outputs found
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
Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data
Audio Word2Vec offers vector representations of fixed dimensionality for
variable-length audio segments using Sequence-to-sequence Autoencoder (SA).
These vector representations are shown to describe the sequential phonetic
structures of the audio segments to a good degree, with real world applications
such as query-by-example Spoken Term Detection (STD). This paper examines the
capability of language transfer of Audio Word2Vec. We train SA from one
language (source language) and use it to extract the vector representation of
the audio segments of another language (target language). We found that SA can
still catch phonetic structure from the audio segments of the target language
if the source and target languages are similar. In query-by-example STD, we
obtain the vector representations from the SA learned from a large amount of
source language data, and found them surpass the representations from naive
encoder and SA directly learned from a small amount of target language data.
The result shows that it is possible to learn Audio Word2Vec model from
high-resource languages and use it on low-resource languages. This further
expands the usability of Audio Word2Vec.Comment: arXiv admin note: text overlap with arXiv:1603.0098
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