15,741 research outputs found
Deep semi-supervised segmentation with weight-averaged consistency targets
Recently proposed techniques for semi-supervised learning such as Temporal
Ensembling and Mean Teacher have achieved state-of-the-art results in many
important classification benchmarks. In this work, we expand the Mean Teacher
approach to segmentation tasks and show that it can bring important
improvements in a realistic small data regime using a publicly available
multi-center dataset from the Magnetic Resonance Imaging (MRI) domain. We also
devise a method to solve the problems that arise when using traditional data
augmentation strategies for segmentation tasks on our new training scheme.Comment: 8 pages, 1 figure, accepted for DLMIA/MICCA
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
A crucial step in processing speech audio data for information extraction,
topic detection, or browsing/playback is to segment the input into sentence and
topic units. Speech segmentation is challenging, since the cues typically
present for segmenting text (headers, paragraphs, punctuation) are absent in
spoken language. We investigate the use of prosody (information gleaned from
the timing and melody of speech) for these tasks. Using decision tree and
hidden Markov modeling techniques, we combine prosodic cues with word-based
approaches, and evaluate performance on two speech corpora, Broadcast News and
Switchboard. Results show that the prosodic model alone performs on par with,
or better than, word-based statistical language models -- for both true and
automatically recognized words in news speech. The prosodic model achieves
comparable performance with significantly less training data, and requires no
hand-labeling of prosodic events. Across tasks and corpora, we obtain a
significant improvement over word-only models using a probabilistic combination
of prosodic and lexical information. Inspection reveals that the prosodic
models capture language-independent boundary indicators described in the
literature. Finally, cue usage is task and corpus dependent. For example, pause
and pitch features are highly informative for segmenting news speech, whereas
pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2),
Special Issue on Accessing Information in Spoken Audio, September 200
Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation
We present a probabilistic model that uses both prosodic and lexical cues for
the automatic segmentation of speech into topically coherent units. We propose
two methods for combining lexical and prosodic information using hidden Markov
models and decision trees. Lexical information is obtained from a speech
recognizer, and prosodic features are extracted automatically from speech
waveforms. We evaluate our approach on the Broadcast News corpus, using the
DARPA-TDT evaluation metrics. Results show that the prosodic model alone is
competitive with word-based segmentation methods. Furthermore, we achieve a
significant reduction in error by combining the prosodic and word-based
knowledge sources.Comment: 27 pages, 8 figure
A framework for quantification and physical modeling of cell mixing applied to oscillator synchronization in vertebrate somitogenesis
In development and disease, cells move as they exchange signals. One example is found in vertebrate development, during which the timing of segment formation is set by a ‘segmentation clock’, in which oscillating gene expression is synchronized across a population of cells by Delta-Notch signaling. Delta-Notch signaling requires local cell-cell contact, but in the zebrafish embryonic tailbud, oscillating cells move rapidly, exchanging neighbors. Previous theoretical studies proposed that this relative movement or cell mixing might alter signaling and thereby enhance synchronization. However, it remains unclear whether the mixing timescale in the tissue is in the right range for this effect, because a framework to reliably measure the mixing timescale and compare it with signaling timescale is lacking. Here, we develop such a framework using a quantitative description of cell mixing without the need for an external reference frame and constructing a physical model of cell movement based on the data. Numerical simulations show that mixing with experimentally observed statistics enhances synchronization of coupled phase oscillators, suggesting that mixing in the tailbud is fast enough to affect the coherence of rhythmic gene expression. Our approach will find general application in analyzing the relative movements of communicating cells during development and disease.Fil: Uriu, Koichiro. Kanazawa University; JapónFil: Bhavna, Rajasekaran. Max Planck Institute of Molecular Cell Biology and Genetics; Alemania. Max Planck Institute for the Physics of Complex Systems; AlemaniaFil: Oates, Andrew C.. Francis Crick Institute; Reino Unido. University College London; Reino UnidoFil: Morelli, Luis Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina. Max Planck Institute for Molecular Physiology; Alemania. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentin
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