4,796 research outputs found
Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition
We propose to model the acoustic space of deep neural network (DNN)
class-conditional posterior probabilities as a union of low-dimensional
subspaces. To that end, the training posteriors are used for dictionary
learning and sparse coding. Sparse representation of the test posteriors using
this dictionary enables projection to the space of training data. Relying on
the fact that the intrinsic dimensions of the posterior subspaces are indeed
very small and the matrix of all posteriors belonging to a class has a very low
rank, we demonstrate how low-dimensional structures enable further enhancement
of the posteriors and rectify the spurious errors due to mismatch conditions.
The enhanced acoustic modeling method leads to improvements in continuous
speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in
both clean and noisy conditions, where upto 15.4% relative reduction in word
error rate (WER) is achieved
Recognising facial expressions in video sequences
We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base
Cluster-based feedback control of turbulent post-stall separated flows
We propose a novel model-free self-learning cluster-based control strategy
for general nonlinear feedback flow control technique, benchmarked for
high-fidelity simulations of post-stall separated flows over an airfoil. The
present approach partitions the flow trajectories (force measurements) into
clusters, which correspond to characteristic coarse-grained phases in a
low-dimensional feature space. A feedback control law is then sought for each
cluster state through iterative evaluation and downhill simplex search to
minimize power consumption in flight. Unsupervised clustering of the flow
trajectories for in-situ learning and optimization of coarse-grained control
laws are implemented in an automated manner as key enablers. Re-routing the
flow trajectories, the optimized control laws shift the cluster populations to
the aerodynamically favorable states. Utilizing limited number of sensor
measurements for both clustering and optimization, these feedback laws were
determined in only iterations. The objective of the present work is not
necessarily to suppress flow separation but to minimize the desired cost
function to achieve enhanced aerodynamic performance. The present control
approach is applied to the control of two and three-dimensional separated flows
over a NACA 0012 airfoil with large-eddy simulations at an angle of attack of
, Reynolds number and free-stream Mach number . The optimized control laws effectively minimize the flight power
consumption enabling the flows to reach a low-drag state. The present work aims
to address the challenges associated with adaptive feedback control design for
turbulent separated flows at moderate Reynolds number.Comment: 32 pages, 18 figure
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