Beyong lexical meaning probabilistic models for sign language recognition

... SL recognition has focused on classifying the lexical meaning in signs This is understandable since the lexical information in signs does express the main information conveyed through signing For ... Model (MH-HMM) for continuous sign recognition Just as in the case for the BN, the MH-HMM models the probabilistic relationship between lexical meaning and inﬂections, and the information streams ... are from the sign which contains the lexical meaning STUDY Frame (b) is during the transition from YOU to STUDY 1.1.1 Manual signs to express lexical meaning Sign linguists agree that signs have...

Tài liệu Báo cáo khoa học: "Web augmentation of language models for continuous speech recognition of SMS text messages" docx

... empirical study of smoothing techniques for language modeling Computer Speech and Language, 13:359–394 Joshua T Goodman 2001 A bit of progress in language modeling Computer Speech and Language, 15:403–434 ... N-gram language models are then trained separately on the in-domain text and the the ﬁltered web text If the amount of web text is very large, only a subset is used, which consists of the parts of ... (Section 2.2.1) for the same number of queries Also results from language modeling and speech recognition experiments favored statistical querying 2.3 Web collections obtained For the speech recognition...

Báo cáo khoa học: "Re-Ranking Models For Spoken Language Understanding Marco Dinarelli University of Trento Italy" potx

... C) of word and concept sequences from an annotated corpus: k i=1 In the context of Spoken Language Understanding (SLU), concept classiﬁcation is the task of associating the best sequence of concepts ... al., 2005) for development and evaluation of spoken understanding models and linguistic studies The corpus is composed of 1257 dialogs, from 250 different speakers, acquired with a Wizard of Oz (WOZ) ... SRILM tools and then converted to an FST This allows the use of a wide set of stochastic language models (both back-off and interpolated models with several discounting techniques like Good-Turing,...

Báo cáo khoa học: "Tree Representations in Probabilistic Models for Extended Named Entities Detection" ppt

... discussed in the introduction and in section 2, since CRFs and models for parsing constitute the main issue in our work, we discuss some important models here Beyond the models for parsing discussed in ... training time, which is a very good point when dealing with noisy data and big set of features 176 Models for Parsing Trees The models used in this work for parsing entity trees refer to the models ... such formulation Tree Representations for Extended Named Entities As discussed in (Johnson, 1998), an important point for a parsing algorithm is the representation of trees being parsed Changing...

Báo cáo khoa học: "Connectionist Models for Natural Language Processing" doc

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Báo cáo khoa học: "Comparison of Alignment Templates and Maximum Entropy Models for Natural Language Understanding" docx

... architecture of the ME approach is summarized in Figure For our approach, we determine the corresponding formal target language concept for each word of a natural language input Therefore, we distinguish ... Pr(e) Language Model 0 Target Language Text Figure 2: Architecture of the translation approach based on the source-channel paradigm Figure 3: Example of alignment templates for representing a natural ... Communication and Technology, pages 2205-2208, Aalborg, Denmark, September S Miller, R Bobrow, R Ingria, and R Schwartz 1994 Hidden understanding models of natural language In Proceedings of the Association...

probabilistic models for unsupervised learning

... Uses of Unsupervised Learning $ data compression $ outlier detection $ classication $ make other learning tasks easier $ a theory of human learning and perception Probabilistic Models % A probabilistic ... ÔĂ Unsupervised learning: The goal of the machine is to build representations from that can be used for reasoning, decision making, predicting things, communicating etc Reinforcement learning: ... Intractability For many probabilistic models of interest, exact inference is not computationally feasible This occurs for two (main) reasons: r distributions may have complicated forms (non-linearities...

conditional random fields- probabilistic models for segmenting and labeling sequence data

... likelihood convergence Conditional Random Fields In what follows, X is a random variable over data sequences to be labeled, and Y is a random variable over corresponding label sequences All components ... minima Conditional random fields offer a unique combination of properties: discriminatively trained models for sequence segmentation and labeling; combination of arbitrary, overlapping and agglomerative ... techniques of conditional random fields Related Work and Conclusions As far as we know, the present work is the first to combine the benefits of conditional models with the global normalization of random...

Báo cáo hóa học: " Research Article Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition" pdf

... introduces the proposed feature extraction schemes Mainly, they include motion representation through motion estimation, telescopic vector composition, motion residuals, and polar accumulated diﬀerences ... coding Feature vector (a) Time-dependent feature extraction In this approach, the motion vectors of each two successive images are estimated and split into their x and y components Each motion vector ... as motion; setting it to a high value results in discarding some motion information, and so forth Following [2], the value of the threshold was determined empirically and set to the mean intensity...

Probabilistic models for reliability assessment of ageing equipment and maintenance optimization

... condition of the equipment CI Costs of performing an activity of inspection CM Costs of performing an activity of minor maintenance CMM Costs of performing an activity of major maintenance CF Costs of ... optimal maintenance policies, the effect of inspection and maintenance should be quantified in terms of reliability and costs Probabilistic maintenance models [7-24] are preferably used for this ... use of this circuit breaker maintenance model, several analyses will be performed to study the effect of maintenance on reliability and costs Considering reliability and cost trade-off, this maintenance...

báo cáo hóa học:" Research Article Compact Acoustic Models for Embedded Speech Recognition" pot

... Conference on Speech Communication and Technology (Eurospeech ’99), pp 1515–1518, Budapest, Hungary, September 1999 [10] J Park and H Ko, “Achieving a reliable compact acoustic model for embedded speech ... whole acoustic space and subword units are separatly modelled At the upper level, a general GMM models the speech signal, state-dependent models being obtained by applying compact transformations ... models, Gaussian mixtures encode not only phoneme-speciﬁc information but also some general information about speech, we propose an approach that aims at limiting the redundancy in acoustic models...

Towards subject independent sign language recognition

... identiﬁed 2.5 Issue 4: Signer Independence A practical requirement for a sign language recognition system is to be signer independent Presently, many of the continuous sign language recognition works ... the ﬁrst sign YOU for signer is relatively higher than that for signer in relation to their bodies In addition, signer signs PRINTING twice while signer signs it once Variations in sign appearance ... the art in sign 22 Related Works and Overview of Proposed Approach language recognition and discuss the major issues in continuous signing 2.1.1 Recognition of Continuous Signing Sign language...

Tài liệu Báo cáo khoa học: "Incremental Syntactic Language Models for Phrase-based Translation" pptx

... (2007) use supertag n-gram LMs Syntactic language models have also been explored with tree-based translation models Charniak et al (2003) use syntactic language models to rescore the output of ... rigorous formal deﬁnition of incremental syntactic languages models, and detailed what steps are necessary to incorporate such LMs into phrase-based decoding We integrated an incremental syntactic language ... seeks to incorporate largescale n-gram language models in conjunction with incremental syntactic language models The added decoding time cost of our syntactic language model is very high By increasing...

Tài liệu Báo cáo khoa học: "Towards History-based Grammars: Using Richer Models for Probabilistic Parsing*" docx

... parameters for this model using decision trees The history-based grammar model provides a mechanism for taking advantage of contextual information from anywhere in the discourse history Using decision ... difficulty in applying probabilistic models to natural language is deciding what aspects of the sentence and the discourse are relevant to the model Most previous probabilistic models of parsing assume ... very rich probabilistic models of context In this work, we present a model, the history-based grammar model, which incorporates a very rich model of context, and we describe a technique for estimating...

... Human-Computer Dialogue Simulation Using Hidden Markov Models In Proc of ASRU, pages 290–295 Nina Dethlefs and Heriberto Cuay´ huitl 2010 Hia erarchical Reinforcement Learning for Adaptive Text Generation ... probability, derived from the Forward algorithm, of an observation sequence to inform the agent’s learning process r =            +1 for for -2 for     P (w0 wn ) for       -1 reaching ... reward for executing action a in state s and then following policy π ∗ i j We use HSMQ -Learning (Dietterich, 1999) to learn a hierarchy of generation policies 656 3.2 Hidden Markov Models for NLG...

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