132 research outputs found
Attentive Adversarial Learning for Domain-Invariant Training
Adversarial domain-invariant training (ADIT) proves to be effective in
suppressing the effects of domain variability in acoustic modeling and has led
to improved performance in automatic speech recognition (ASR). In ADIT, an
auxiliary domain classifier takes in equally-weighted deep features from a deep
neural network (DNN) acoustic model and is trained to improve their
domain-invariance by optimizing an adversarial loss function. In this work, we
propose an attentive ADIT (AADIT) in which we advance the domain classifier
with an attention mechanism to automatically weight the input deep features
according to their importance in domain classification. With this attentive
re-weighting, AADIT can focus on the domain normalization of phonetic
components that are more susceptible to domain variability and generates deep
features with improved domain-invariance and senone-discriminativity over ADIT.
Most importantly, the attention block serves only as an external component to
the DNN acoustic model and is not involved in ASR, so AADIT can be used to
improve the acoustic modeling with any DNN architectures. More generally, the
same methodology can improve any adversarial learning system with an auxiliary
discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3%
relative WER improvements, respectively, over a multi-conditional model and a
strong ADIT baseline.Comment: 5 pages, 1 figure, ICASSP 201
Adversarial Speaker Adaptation
We propose a novel adversarial speaker adaptation (ASA) scheme, in which
adversarial learning is applied to regularize the distribution of deep hidden
features in a speaker-dependent (SD) deep neural network (DNN) acoustic model
to be close to that of a fixed speaker-independent (SI) DNN acoustic model
during adaptation. An additional discriminator network is introduced to
distinguish the deep features generated by the SD model from those produced by
the SI model. In ASA, with a fixed SI model as the reference, an SD model is
jointly optimized with the discriminator network to minimize the senone
classification loss, and simultaneously to mini-maximize the SI/SD
discrimination loss on the adaptation data. With ASA, a senone-discriminative
deep feature is learned in the SD model with a similar distribution to that of
the SI model. With such a regularized and adapted deep feature, the SD model
can perform improved automatic speech recognition on the target speaker's
speech. Evaluated on the Microsoft short message dictation dataset, ASA
achieves 14.4% and 7.9% relative word error rate improvements for supervised
and unsupervised adaptation, respectively, over an SI model trained from 2600
hours data, with 200 adaptation utterances per speaker.Comment: 5 pages, 2 figures, ICASSP 201
Conditional Teacher-Student Learning
The teacher-student (T/S) learning has been shown to be effective for a
variety of problems such as domain adaptation and model compression. One
shortcoming of the T/S learning is that a teacher model, not always perfect,
sporadically produces wrong guidance in form of posterior probabilities that
misleads the student model towards a suboptimal performance. To overcome this
problem, we propose a conditional T/S learning scheme, in which a "smart"
student model selectively chooses to learn from either the teacher model or the
ground truth labels conditioned on whether the teacher can correctly predict
the ground truth. Unlike a naive linear combination of the two knowledge
sources, the conditional learning is exclusively engaged with the teacher model
when the teacher model's prediction is correct, and otherwise backs off to the
ground truth. Thus, the student model is able to learn effectively from the
teacher and even potentially surpass the teacher. We examine the proposed
learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker
adaptation on Microsoft short message dictation dataset. The proposed method
achieves 9.8% and 12.8% relative word error rate reductions, respectively, over
T/S learning for environment adaptation and speaker-independent model for
speaker adaptation.Comment: 5 pages, 1 figure, ICASSP 201
Hypothesis testing for medical imaging analysis via the smooth Euler characteristic transform
Shape-valued data are of interest in applied sciences, particularly in
medical imaging. In this paper, inspired by a specific medical imaging example,
we introduce a hypothesis testing method via the smooth Euler characteristic
transform to detect significant differences among collections of shapes. Our
proposed method has a solid mathematical foundation and is computationally
efficient. Through simulation studies, we illustrate the performance of our
proposed method. We apply our method to images of lung cancer tumors from the
National Lung Screening Trial database, comparing its performance to a
state-of-the-art machine learning model
Randomness of Shapes and Statistical Inference on Shapes via the Smooth Euler Characteristic Transform
In this article, we establish the mathematical foundations for modeling the
randomness of shapes and conducting statistical inference on shapes using the
smooth Euler characteristic transform. Based on these foundations, we propose
two parametric algorithms for testing hypotheses on random shapes. Simulation
studies are presented to validate our mathematical derivations and to compare
our algorithms with state-of-the-art methods to demonstrate the utility of our
proposed framework. As real applications, we analyze a data set of mandibular
molars from four genera of primates and show that our algorithms have the power
to detect significant shape differences that recapitulate known morphological
variation across suborders. Altogether, our discussions bridge the following
fields: algebraic and computational topology, probability theory and stochastic
processes, Sobolev spaces and functional analysis, statistical inference, and
geometric morphometrics.Comment: 99 page
- …