31,918 research outputs found
Source domain data selection for improved transfer learning targeting dysarthric speech recognition
This paper presents an improved transfer learning framework applied to robust personalised speech recognition models for speakers with dysarthria. As the baseline of transfer learning, a state-of-theart CNN-TDNN-F ASR acoustic model trained solely on source domain data is adapted onto the target domain via neural network weight adaptation with the limited available data from target dysarthric speakers. Results show that linear weights in neural layers play the most important role for an improved modelling of dysarthric speech evaluated using UASpeech corpus, achieving averaged 11.6% and 7.6% relative recognition improvement in comparison to the conventional speaker-dependent training and data combination, respectively. To further improve the transferability towards target domain, we propose an utterance-based data selection of the source domain data based on the entropy of posterior probability, which is analysed to statistically obey a Gaussian distribution.
Compared to a speaker-based data selection via dysarthria similarity measure, this allows for a more accurate selection of the potentially beneficial source domain data for either increasing the target domain training pool or constructing an intermediate domain for incremental transfer learning, resulting in a further absolute recognition performance improvement of nearly 2% added to transfer learning baseline for speakers with moderate to severe dysarthria
Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information
Applying people detectors to unseen data is challenging since patterns distributions, such
as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ
from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt
frame by frame people detectors during runtime classification, without requiring any additional
manually labeled ground truth apart from the offline training of the detection model. Such adaptation
make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors
estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation
discriminates between relevant instants in a video sequence, i.e., identifies the representative frames
for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration
(i.e., detection threshold) of each detector under analysis, maximizing the mutual information to
obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not
require training the detectors for each new scenario and uses standard people detector outputs, i.e.,
bounding boxes. The experimental results demonstrate that the proposed approach outperforms
state-of-the-art detectors whose optimal threshold configurations are previously determined and
fixed from offline training dataThis work has been partially supported by the Spanish government under the project TEC2014-53176-R
(HAVideo
Learning to select data for transfer learning with Bayesian Optimization
Domain similarity measures can be used to gauge adaptability and select
suitable data for transfer learning, but existing approaches define ad hoc
measures that are deemed suitable for respective tasks. Inspired by work on
curriculum learning, we propose to \emph{learn} data selection measures using
Bayesian Optimization and evaluate them across models, domains and tasks. Our
learned measures outperform existing domain similarity measures significantly
on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We
show the importance of complementing similarity with diversity, and that
learned measures are -- to some degree -- transferable across models, domains,
and even tasks.Comment: EMNLP 2017. Code available at:
https://github.com/sebastianruder/learn-to-select-dat
Domain Adaptation for Statistical Classifiers
The most basic assumption used in statistical learning theory is that
training data and test data are drawn from the same underlying distribution.
Unfortunately, in many applications, the "in-domain" test data is drawn from a
distribution that is related, but not identical, to the "out-of-domain"
distribution of the training data. We consider the common case in which labeled
out-of-domain data is plentiful, but labeled in-domain data is scarce. We
introduce a statistical formulation of this problem in terms of a simple
mixture model and present an instantiation of this framework to maximum entropy
classifiers and their linear chain counterparts. We present efficient inference
algorithms for this special case based on the technique of conditional
expectation maximization. Our experimental results show that our approach leads
to improved performance on three real world tasks on four different data sets
from the natural language processing domain
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