CRF-Based Phrase Boundary Detection Trained on Large-Scale TTS Speech Corpora

2017 ◽  
pp. 272-281 ◽  
Author(s):  
Markéta Jůzová
Keyword(s):  
Large Scale ◽  
Boundary Detection ◽  
Phrase Boundary ◽  
Speech Corpora

scholarly journals Boundary Detection Method for Large-Scale Coverage Holes in Wireless Sensor Network Based on Minimum Critical Threshold Constraint

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Rong Jing ◽  
Lingfu Kong ◽  
Liang Kong
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Large Scale ◽  
Optimization Problem ◽  
Boundary Detection ◽  
Wireless Sensor ◽  
Critical Threshold ◽  
Coverage Hole ◽  
Hole Boundary ◽  
Coverage Holes

The existing coverage hole boundary detection methods cannot detect large-scale coverage hole boundary in wireless sensor network quickly and efficiently. Aiming at this problem, a boundary detection method for large-scale coverage holes in wireless sensor network based on minimum critical threshold constraint is proposed. Firstly, the optimization problem of minimum critical threshold is highlighted, and its formulaic description is constructed according to probabilistic sensing model. On the basis of this, the distributed gradient information is used to approximately solve the optimization problem. After that, local-scale rough boundary detection algorithm incorporating the minimum critical threshold and its iterative thinning algorithm are proposed according to blocking flow theory. The experimental results show that the proposed method has low computational complexity and network overhead when detecting large-scale coverage hole boundary in wireless sensor network.

scholarly journals An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 537 ◽  
Author(s):  
Huafeng Wu ◽  
Qingshun Meng ◽  
Jiangfeng Xian ◽  
Xiaojun Mei ◽  
Christophe Claramunt ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Entropy ◽  
Large Scale ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Environment Monitoring ◽  
Event Boundary

Wireless Sensor Networks (WSNs) have been extensively applied in ecological environment monitoring. Typically, event boundary detection is an effective method to determine the scope of an event area in large-scale environment monitoring. This paper proposes a novel lightweight Entropy based Event Boundary Detection algorithm (EEBD) in WSNs. We first develop a statistic model using information entropy to figure out the probability that a sensor is a boundary sensor. The EEBD is independently executed on each wireless sensor in order to judge whether it is a boundary sensor node, by comparing the values of entropy against the threshold which depends on the boundary width. Simulation results demonstrate that the EEBD is computable and offers valuable detection accuracy of boundary nodes with both low and high network node density. This study also includes experiments that verify the EEBD which is applicable in a real ocean environmental monitoring scenario using WSNs.

scholarly journals An empirical generative framework for computational modeling of language acquisition

2010 ◽  
Vol 37 (3) ◽  
pp. 671-703 ◽  
Author(s):  
Heidi R. Waterfall ◽  
Ben Sandbank ◽  
Luca Onnis ◽  
Shimon Edelman
Keyword(s):  
Language Acquisition ◽  
Computer Model ◽  
Large Scale ◽  
Generative Grammar ◽  
Generation Model ◽  
Computational Results ◽  
Modeling Framework ◽  
Speech Corpora ◽  
Quantitative Performance ◽  
Corpus Data

ABSTRACTThis paper reports progress in developing a computer model of language acquisition in the form of (1) a generative grammar that is (2) algorithmically learnable from realistic corpus data, (3) viable in its large-scale quantitative performance and (4) psychologically real. First, we describe new algorithmic methods for unsupervised learning of generative grammars from raw CHILDES data and give an account of the generative performance of the acquired grammars. Next, we summarize findings from recent longitudinal and experimental work that suggests how certain statistically prominent structural properties of child-directed speech may facilitate language acquisition. We then present a series of new analyses of CHILDES data indicating that the desired properties are indeed present in realistic child-directed speech corpora. Finally, we suggest how our computational results, behavioral findings, and corpus-based insights can be integrated into a next-generation model aimed at meeting the four requirements of our modeling framework.

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