Phonetic comparison, varieties, and networks

Diachronica ◽  
2010 ◽  
Vol 27 (2) ◽  
pp. 325-340 ◽  
Author(s):  
Jennifer Sullivan ◽  
April McMahon
Keyword(s):  
Language Varieties ◽  
Small Set ◽  
Phonetic Distance ◽  
Basic Vocabulary ◽  
Language Divergence

While his eponymous basic vocabulary lists and the study of language divergence may be Swadesh’s most appreciated legacies, we demonstrate that phonetic quantification of language varieties also follows very much in the tradition of Swadesh’s own work. We compare a few different measures of phonetic distance on a very small set of data from Germanic varieties, showing the influence of lexicostatistics and the relevance of Swadesh’s ‘Mesh Principle’. What we emphasise overall is that Swadesh’s influence is palpable, even in domains outside those for which he is best remembered.

scholarly journals Bayesian phylolinguistics infers the internal structure and the time-depth of the Turkic language family

2020 ◽  
Vol 5 (1) ◽  
pp. 39-53 ◽  
Author(s):  
Alexander Savelyev ◽  
Martine Robbeets
Keyword(s):  
Internal Structure ◽  
Language Family ◽  
Language Varieties ◽  
Quantitative Basis ◽  
Turkic Languages ◽  
Divergence Dates ◽  
Basic Vocabulary ◽  
Historical Comparative ◽  
Lexical Data ◽  
Bayesian Phylogenetic Inference

Abstract Despite more than 200 years of research, the internal structure of the Turkic language family remains subject to debate. Classifications of Turkic so far are based on both classical historical–comparative linguistic and distance-based quantitative approaches. Although these studies yield an internal structure of the Turkic family, they cannot give us an understanding of the statistical robustness of the proposed branches, nor are they capable of reliably inferring absolute divergence dates, without assuming constant rates of change. Here we use computational Bayesian phylogenetic methods to build a phylogeny of the Turkic languages, express the reliability of the proposed branches in terms of probability, and estimate the time-depth of the family within credibility intervals. To this end, we collect a new dataset of 254 basic vocabulary items for thirty-two Turkic language varieties based on the recently introduced Leipzig–Jakarta list. Our application of Bayesian phylogenetic inference on lexical data of the Turkic languages is unprecedented. The resulting phylogenetic tree supports a binary structure for Turkic and replicates most of the conventional sub-branches in the Common Turkic branch. We calculate the robustness of the inferences for subgroups and individual languages whose position in the tree seems to be debatable. We infer the time-depth of the Turkic family at around 2100 years before present, thus providing a reliable quantitative basis for previous estimates based on classical historical linguistics and lexicostatistics.

Phonetic and Lexical Predictors of Intelligibility

2008 ◽  
Vol 2 (1-2) ◽  
pp. 63-81 ◽  
Author(s):  
Charlotte Gooskens ◽  
Wilbert Heeringa ◽  
Karin Beijering
Keyword(s):  
Regression Analyses ◽  
Language Varieties ◽  
Related Language ◽  
Scandinavian Language ◽  
Phonetic Distance

In the present investigation, the intelligibility of 17 Scandinavian language varieties and standard Danish was assessed among young Danes from Copenhagen. In addition, distances between standard Danish and each of the 17 varieties were measured at the lexical level and at different phonetic levels. In order to determine how well these linguistic levels can predict intelligibility, we correlated the intelligibility scores with the linguistic distances and we carried out a number of regression analyses. The results show that for this particular set of closely related language varieties phonetic distance is a better predictor of intelligibility than lexical distance. Consonant substitutions, vowel insertions and vowel shortenings contribute significantly to the prediction of intelligibility.

Relationships Between Subjective Ratings and Objective Measures of Performance in Speechreading Sentences

1997 ◽  
Vol 40 (4) ◽  
pp. 900-911 ◽  
Author(s):  
Marilyn E. Demorest ◽  
Lynne E. Bernstein
Keyword(s):  
Hearing Impairment ◽  
Performance Evaluations ◽  
Objective Measures ◽  
Large Individual ◽  
Subjective Ratings ◽  
Objective Performance ◽  
Phonetic Distance ◽  
Stimulus Length ◽  
Measures Of Performance ◽  
Confidence Scale

Ninety-six participants with normal hearing and 63 with severe-to-profound hearing impairment viewed 100 CID Sentences (Davis & Silverman, 1970) and 100 B-E Sentences (Bernstein & Eberhardt, 1986b). Objective measures included words correct, phonemes correct, and visual-phonetic distance between the stimulus and response. Subjective ratings were made on a 7-point confidence scale. Magnitude of validity coefficients ranged from .34 to .76 across materials, measures, and groups. Participants with hearing impairment had higher levels of objective performance, higher subjective ratings, and higher validity coefficients, although there were large individual differences. Regression analyses revealed that subjective ratings are predictable from stimulus length, response length, and objective performance. The ability of speechreaders to make valid performance evaluations was interpreted in terms of contemporary word recognition models.

Arrangement Of Monomeric Units In Fibrin

1979 ◽  
Author(s):  
Jan Hermans
Keyword(s):  
Fiber Axis ◽  
High Symmetry ◽  
Linear Polymers ◽  
Fiber Volume ◽  
High Fiber ◽  
Rotation Axes ◽  
Small Set ◽  
Helical Turn ◽  
The One ◽  
Kinetics Of

Measurements of light scattering have given much information about formation and properties of fibrin. These studies have determined mass-length ratio of linear polymers (protofibrils) and of fibers, kinetics of polymerization and of lateral association and volume-mass ratio of thick fibers. This ratio is 5 to 1. On the one hand, this high value suggests that the fiber contains channels that allow the diffusion of enzymes such as Factor XHIa and plasmin; on the other hand, the high value appears paradoxical for a stiff fiber made up of elongated units (fibrin monomers) arranged in parallel. Such a high fiber volume is a property of only a small set out of many high-symmetry models of fibrin, which may be constructed from overlapping three-domain monomers which are arranged into strands, are aligned nearly parallel to the fiber axis and make adequate longitudinal and lateral contacts. These models contain helical protofibrils related to each other by rotation axes parallel to the fiber axis. The protofibrils may contain 2, 3 or 4 monomers per helical turn and there are four possible symmetries. A large specific volume is achieved if the ends of each monomer are slightly displaced from the protofibril axis, either by a shift or by a tilt of the monomer. The fiber containing tilted monomers is more highly interconnected; the two ends of a tilted monomer form lateral contacts with different adjacent protofibrils, whereas the two ends of a non-tilted monomer contact the same adjacent protofibril(s).

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

Gay (White) Male Speech and the E(race)sure of Identity

2020 ◽  
Author(s):  
Brianna R. Cornelius
Keyword(s):  
Racial Identity ◽  
Language Use ◽  
White Male ◽  
The United States ◽  
Gay Identity ◽  
Community Based ◽  
Language Varieties ◽  
Gay Male ◽  
Men Of Color ◽  
African American Language

Although a notable body of work has emerged describing gay male speech (GMS), its overlap with African American language (AAL) remains comparatively understudied. This chapter explores the assumption of whiteness that has informed research on gay identity and precluded intersectional considerations in sociolinguistic research. Examining the importance of racial identity, particularly Blackness, to the construction of gay identity in the United States, the chapter investigates the treatment of GMS as white by default, with the voices of gay men of color considered additive. The desire vs. identity debate in language and sexuality studies contributed to an understanding of gay identity as community-based practice, thereby laying a necessary framework for the study of GMS. However, this framework led to a virtually exclusive focus on white men’s language use. Although efforts to bring a community-based understanding to gay identity have been groundbreaking, the lack of consideration of intersectionality has erased contributions to GMS from racially based language varieties, such as AAL.

scholarly journals Neural network aided approximation and parameter inference of non-Markovian models of gene expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qingchao Jiang ◽  
Xiaoming Fu ◽  
Shifu Yan ◽  
Runlai Li ◽  
Wenli Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Stochastic Dynamics ◽  
Stochastic Simulations ◽  
Parameter Inference ◽  
Biochemical Processes ◽  
Markovian Models ◽  
The Neural Network ◽  
Large Numbers ◽  
Small Set ◽  
Noisy Measurements

AbstractNon-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markovian models by the solutions of much simpler time-inhomogeneous Markovian models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markovian model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markovian models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.

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