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Recent advances in automatic speech recognition have used
large corpora and powerful computational resources to train
complex statistical models from high-dimensional features, to
attempt to capture all the variability found in natural speech.
Such models are difficult to interpret and may be fragile, and
contradict or ignore knowledge of human speech produc-
tion and perception. We report progress towards phoneme
recognition using a model of speech which employs very few
parameters and which is more faithful to the dynamics and
This paper presents a feature learning approach for speaker identification that is based on nonnegative matrix factorisation. Recent studies have shown that with such models, the dictionary atoms can represent well the speaker identity. The approaches proposed so far focused only on speaker variability and not on session variability. However, this later point is a crucial aspect in the success of the I-vector approach that is now the state-of-the-art in speaker identification.
X-ray sources are polychromatic. Ignoring this fact when performing reconstruction leads to artifacts, such as cupping and streaking, in reconstructed images. We first propose a new model parameterization that allows for blind correction of these artifacts and then develop reconstruction algorithms based on this parameterization.
Here, blind correction means that we do not know
- incident spectrum (which is an X-ray machine characteristic) and
- mass attenuation (inspected material).
Hyperspectral unmixing consists in determining the reference spectral
signatures composing a hyperspectral image and their relative
abundance fractions in each pixel. In practice, the identified signatures
may be affected by a significant spectral variability resulting
for instance from the temporal evolution of the imaged scene. This
phenomenon can be accounted for by using a perturbed linear mixing
model. This paper studies an online estimation algorithm for the
parameters of this extended linear mixing model. This algorithm is