873 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
Semantic relatedness based re-ranker for text spotting
Applications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based neural approaches. We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness. The scenario is text spotting in the wild, where a text in an image (e.g. street sign, advertisement or bus destination) must be identified and recognized. Our goal is to improve the performance of vision systems by leveraging semantic information. Our rationale is that the text to be spotted is often related to the image context in which it appears (word pairs such as Delta–airplane, or quarters–parking are not similar, but are clearly related). We show how learning a word-to-word or word-to-sentence relatedness score can improve the performance of text spotting systems up to 2.9 points, outperforming other measures in a benchmark dataset.Peer ReviewedPostprint (author's final draft
Deep Spoken Keyword Spotting:An Overview
Spoken keyword spotting (KWS) deals with the identification of keywords in
audio streams and has become a fast-growing technology thanks to the paradigm
shift introduced by deep learning a few years ago. This has allowed the rapid
embedding of deep KWS in a myriad of small electronic devices with different
purposes like the activation of voice assistants. Prospects suggest a sustained
growth in terms of social use of this technology. Thus, it is not surprising
that deep KWS has become a hot research topic among speech scientists, who
constantly look for KWS performance improvement and computational complexity
reduction. This context motivates this paper, in which we conduct a literature
review into deep spoken KWS to assist practitioners and researchers who are
interested in this technology. Specifically, this overview has a comprehensive
nature by covering a thorough analysis of deep KWS systems (which includes
speech features, acoustic modeling and posterior handling), robustness methods,
applications, datasets, evaluation metrics, performance of deep KWS systems and
audio-visual KWS. The analysis performed in this paper allows us to identify a
number of directions for future research, including directions adopted from
automatic speech recognition research and directions that are unique to the
problem of spoken KWS
Spoken command recognition for robotics
In this thesis, I investigate spoken command recognition technology for robotics. While high
robustness is expected, the distant and noisy conditions in which the system has to operate
make the task very challenging. Unlike commercial systems which all rely on a "wake-up"
word to initiate the interaction, the pipeline proposed here directly detect and recognizes
commands from the continuous audio stream. In order to keep the task manageable despite
low-resource conditions, I propose to focus on a limited set of commands, thus trading off
flexibility of the system against robustness.
Domain and speaker adaptation strategies based on a multi-task regularization paradigm
are first explored. More precisely, two different methods are proposed which rely on a tied
loss function which penalizes the distance between the output of several networks. The first
method considers each speaker or domain as a task. A canonical task-independent network is
jointly trained with task-dependent models, allowing both types of networks to improve by
learning from one another. While an improvement of 3.2% on the frame error rate (FER) of
the task-independent network is obtained, this only partially carried over to the phone error
rate (PER), with 1.5% of improvement. Similarly, a second method explored the parallel
training of the canonical network with a privileged model having access to i-vectors. This
method proved less effective with only 1.2% of improvement on the FER.
In order to make the developed technology more accessible, I also investigated the use
of a sequence-to-sequence (S2S) architecture for command classification. The use of an
attention-based encoder-decoder model reduced the classification error by 40% relative to a
strong convolutional neural network (CNN)-hidden Markov model (HMM) baseline, showing
the relevance of S2S architectures in such context. In order to improve the flexibility of the
trained system, I also explored strategies for few-shot learning, which allow to extend the
set of commands with minimum requirements in terms of data. Retraining a model on the
combination of original and new commands, I managed to achieve 40.5% of accuracy on the
new commands with only 10 examples for each of them. This scores goes up to 81.5% of
accuracy with a larger set of 100 examples per new command. An alternative strategy, based
on model adaptation achieved even better scores, with 68.8% and 88.4% of accuracy with 10
and 100 examples respectively, while being faster to train. This high performance is obtained
at the expense of the original categories though, on which the accuracy deteriorated. Those
results are very promising as the methods allow to easily extend an existing S2S model with
minimal resources.
Finally, a full spoken command recognition system (named iCubrec) has been developed
for the iCub platform. The pipeline relies on a voice activity detection (VAD) system to
propose a fully hand-free experience. By segmenting only regions that are likely to contain
commands, the VAD module also allows to reduce greatly the computational cost of the
pipeline. Command candidates are then passed to the deep neural network (DNN)-HMM
command recognition system for transcription. The VoCub dataset has been specifically
gathered to train a DNN-based acoustic model for our task. Through multi-condition training
with the CHiME4 dataset, an accuracy of 94.5% is reached on VoCub test set. A filler model,
complemented by a rejection mechanism based on a confidence score, is finally added to the
system to reject non-command speech in a live demonstration of the system
- …