The estimation of stochastic context-free grammars using the Inside-Outside algorithm

Using an entropy argument, it is shown that stochastic context-free grammars (SCFG's) can model sources with hidden branching processes more efficiently than stochastic regular grammars (or equivalently HMM's). However, the automatic estimation of SCFG's using the Inside-Outside algorithm is limited in practice by its O(n3) complexity. In this paper, a novel pre-training algorithm is described which can give significant computational savings. Also, the need for controlling the way that non-terminals are allocated to hidden processes is discussed and a solution is presented in the form of a grammar minimization procedure. © 1990.

Applications of stochastic context-free grammars using the Inside-Outside algorithm

This paper describes two applications in speech recognition of the use of stochastic context-free grammars (SCFGs) trained automatically via the Inside-Outside Algorithm. First, SCFGs are used to model VQ encoded speech for isolated word recognition and are compared directly to HMMs used for the same task. It is shown that SCFGs can model this low-level VQ data accurately and that a regular grammar based pre-training algorithm is effective both for reducing training time and obtaining robust solutions. Second, an SCFG is inferred from a transcription of the speech used to train a phoneme-based recognizer in an attempt to model phonotactic constraints. When used as a language model, this SCFG gives improved performance over a comparable regular grammar or bigram. © 1991.

Voluntary guidelines for securing sustainable small-scale fisheries in the context of food security and poverty eradication: summary

The Voluntary Guidelines for Securing Sustainable Small-scale Fisheries in the Context of Food Security and Poverty Eradication (SSF Guidelines) were adopted by member countries of the Food and Agriculture Organization of the United Nations (FAO) and were officially approved as an international instrument in June 2014. What is very special about the SSF Guidelines is that it was created as a result of a very long history of the struggles of small-scale fishworkers around the world appealing for greater recognition of their status and their role in the fisheries sector of their countries. These Guidelines have 100 paragraphs which are distributed across 13 sections. This document is only a summation of the contents of the Guidelines. It was produced for ICSF by John Kurien, founder Member of ICSF, who has worked for the last four decades with small-scale fishing communities in many areas around the world, particularly in Kerala, India.

Towards socially just and sustainable fisheries: ICSF workshop on implementing the FAO voluntary guidelines for securing sustainable small-scale fisheries in the context of food security and poverty eradication (SSF guidelines) report, 21 to 24 July 2014 , Puducherry, India

This publication is a report of the proceedings of the ICSF Pondy Workshop, which focused on the FAO’s Voluntary Guidelines for Securing Sustainable Small-scale Fisheries in the Context of Food Security and Poverty Eradication (SSF Guidelines). The workshop brought together 71 participants from 20 countries representing civil society organizations, governments, FAO, academia and fishworker organizations from both the marine and inland fisheries sectors. This report will be found useful for fishworker organizations, researchers, policymakers, members of civil society and anyone interested in small-scale fisheries, food security and poverty eradication.

Report of the ICSF-BOBLME India (East Coast) workshop: implementing the FAO voluntary guidelines for securing sustainable small-scale fisheries in the context of food security and poverty eradication (SSF guidelines), 6-7 March 2015, Chennai, India

The workshop titled, ICSF-BOBLME India (East Coast) Workshop on Implementing the FAO Voluntary Guidelines for Securing Sustainable Small-scale fisheries in the Context of Food Security and Poverty Eradication (SSF Guidelines) was organized by ICSFin collaboration with BOBLME project.The workshop was the third in a series of consultations held in 2015 across the globe to promote the ownership of the SSF Guidelines among different stakeholders. In the run –up to the workshop, ICSF, with support from BOBLME conducted six consultation meetings with fishworkers and fishworker organizations along the east coast of India in January and February 2015. One hundred participants from India’s eastern coastal states of West Bengal, Odisha, Andhra Pradesh, Tamil Nadu and Puducherry, including women fishworkers, representatives of fishworker organizations, representatives from Department of Fisheries and other concerned departments at state and central level, Multilateral agencies, Inter-governmental organizations, Research Institutions met at Chennai, 6-7 March, 2015. The workshop was structured to facilitate active interaction and discussion among participants, taking into account linguistic diversity and the contextual differences of the marine and inland sectors. This publication—the proceedings of the Chennai workshop—will be useful for fishworker organizations, researchers, policymakers, members of civil society and anyone interested in fisheries and livelihoods.

Decision tree-based context clustering based on cross validation and hierarchical priors

The standard, ad-hoc stopping criteria used in decision tree-based context clustering are known to be sub-optimal and require parameters to be tuned. This paper proposes a new approach for decision tree-based context clustering based on cross validation and hierarchical priors. Combination of cross validation and hierarchical priors within decision tree-based context clustering offers better model selection and more robust parameter estimation than conventional approaches, with no tuning parameters. Experimental results on HMM-based speech synthesis show that the proposed approach achieved significant improvements in naturalness of synthesized speech over the conventional approaches. © 2011 IEEE.

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics, however, the representations can be engaged based on visual context, and are updated by a single-rate process.