Learning Structured Models for Phone Recognition - Slav Petrov
897–905, Prague, June 2007. cO2007 Association for Computational Linguistics. Learning Structured Models for Phone Recognition. Slav Petrov. Adam Pauls.
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Learning Deep Structured Models - Department of Computer Science
For example, in ob- ject recognition, independently learned segmentation and ... Algorithm: Deep Structured Learning. Repeat until ... Gradient descent for learning deep structured models. data likelihood, i.e. ...... phone recognition. In Proc.
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Structured Speech Modeling - Microsoft Research
paradigm in speech recognition research within the genera- tive modeling framework ... learning, structured modeling, vocal tract resonance. I. INTRODUCTION ..... a multivariate distribution of the VTR targets.2 Each phone- de- pendent target ...
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Learning Structured Models for Recognizing Human Actions
Action Recognition. • Recognize human actions from raw ... and algorithms for structured models of human actions ... Learning hCRF Parameters. • Conditional ...
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Learning Structured Models with the AUC Loss and Its Generalizations
structured models over the AUC loss, show how our ... model that we developed for learning under a Ham-. 841 ...... recognition letters, 27(8):861–874, 2006.
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Structured SVMs for Automatic Speech Recognition
trained using a large (compared to many machine learning tasks) amount of ... e.g., words and phones. Most CSR systems use structured generative models, in the .... must be segmented at a sub-word level, such as phones to yield complete ...
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Structured Discriminative Models For Speech Recognition - Machine
approaches for applying structured discriminative models to ASR, both from the ..... possible segmentation of the observations at the phone and word levels. ... 3For some machine learning tasks structured data and sequence data are used ...
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Unsupervised feature learning for audio classification using
For the phone classification task, MFCC ... the use of deep learning approaches for audio classification. ..... Learning structured models for phone recognition. In.
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Deep Belief Networks for phone recognition - Department of
chine learning problems and this paper applies DBNs to acoustic modeling. ... els (HMMs) to model the sequential structure of speech signals, with local spectral ...
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Spectral Learning of Refinement HMMs - Columbia University
EM on a phoneme recognition task, and is more stable with ... tral methods for learning HMMs (Hsu et al., 2012; ...... ing structured models for phone recognition .
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Structured Discriminative Models for Speech Recognition
machine learning and natural language processing (NLP) research areas are increasingly ... describes recent work in the area of structured discriminative models for ASR. ..... possible segmentation of the observations at the phone and word.
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Softmax-Margin Training for Structured Log-Linear Models
margin learning for structured prediction (Taskar et al., 2003), and show that the method optimizes a bound on risk. ... (2008) for training speech recognition models. ...... Minimum phone error and I-smoothing for improved discrimative training.
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Deep Structured Output Learning for Unconstrained Text Recognition
Apr 10, 2015 ... DEEP STRUCTURED OUTPUT LEARNING FOR ... We show that this entire model (CRF, character predictor, N-gram predictor) can be jointly ... for generic alpha-numeric strings such as number plates or phone numbers.
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Deep Neural Networks for Acoustic Modeling in Speech Recognition
Apr 27, 2012 ... arise when each phone is modelled by a number of different ... states. Using the new learning methods, several different research groups have shown that DNNs can outperform. GMMs at acoustic modeling for speech recognition on a variety of .... them to be good at modeling the structure in the input data.
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TOWARDS STRUCTURED DEEP NEURAL NETWORK FOR
the lexicon and the language model, which are respectively learned separetely from disjoint ... phoneme recognition by learning the relationships between the acoustic vector .... Phone error rate is used as the distance in this paper, but other ...
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Structured Output Layer with Auxiliary Targets for Context
Index Terms: multitask learning, structured output layer, adap- tation, deep neural ... elling for speech recognition by Seide et al. . However, in  .... is a model trained on. 45 position-independent phones (compared to the other models.
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Fully Connected Deep Structured Networks
Mar 9, 2015 ... ... the segmentation models in a piece-wise fashion, fixing the unary weights during learning of the parameters of the pairwise .... Algorithm: Learning Deep Structured Models. Repeat until ..... based phone recognition. In Proc.
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Deep Learning, Graphical Models, EnergyBased Models, Structured
models for “structured prediction” are descendants of discriminative learning methods for speech recognition and word-level handwriting recognition methods from the early 90's. A Tutorial ... phonemes or phones. Handwriting Recognition: the ...
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Sum-Product Networks for Structured Prediction: Context-Specific
Learning, Beijing, China, 2014. JMLR: W&CP ... difficult to learn structured models and even lead to ... labeling tasks in optical character recognition and phone.
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Building machines that learn and think - arXiv.org
Mar 15, 2016 ... neural network advances with more structured cognitive models. 1 Introduction. Artificial ... recognition systems based on deep learning have been deployed in core products on smart phones and the web. The media has also ...
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