79,656 research outputs found
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition
This article provides a unifying Bayesian network view on various approaches
for acoustic model adaptation, missing feature, and uncertainty decoding that
are well-known in the literature of robust automatic speech recognition. The
representatives of these classes can often be deduced from a Bayesian network
that extends the conventional hidden Markov models used in speech recognition.
These extensions, in turn, can in many cases be motivated from an underlying
observation model that relates clean and distorted feature vectors. By
converting the observation models into a Bayesian network representation, we
formulate the corresponding compensation rules leading to a unified view on
known derivations as well as to new formulations for certain approaches. The
generic Bayesian perspective provided in this contribution thus highlights
structural differences and similarities between the analyzed approaches
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
Domain Adaptation is an actively researched problem in Computer Vision. In
this work, we propose an approach that leverages unsupervised data to bring the
source and target distributions closer in a learned joint feature space. We
accomplish this by inducing a symbiotic relationship between the learned
embedding and a generative adversarial network. This is in contrast to methods
which use the adversarial framework for realistic data generation and
retraining deep models with such data. We demonstrate the strength and
generality of our approach by performing experiments on three different tasks
with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and
USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain
adaptation from synthetic to real data. Our method achieves state-of-the art
performance in most experimental settings and by far the only GAN-based method
that has been shown to work well across different datasets such as OFFICE and
DIGITS.Comment: Accepted as spotlight talk at CVPR 2018. Code available here:
https://github.com/yogeshbalaji/Generate_To_Adap
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