LEARNING The objectives of BIOS 781 are to present: OBJECTIVES: 1. basic population and quantitative genetic principles, including classical genetics, chromosomal theory of inheritance, and meiotic recombination 2. an exposure to QTL mapping methods of complex quantitative traits and linkage methods to detect co-segregation with disease 3. methods for assessing marker-disease linkage disequilibrium, including case-control approaches 4. methods for genome-wide association and stratification control.

Genetic Data Analysis

Second language acquisition (SLA) is a field that investigates child and adult SLA from a variety of theoretical perspectives. This article provides a survey of some key areas of concern including formal generative theory and emergentist theory in the areas of morpho-syntax and phonology. The review details the theoretical stance of the two different approaches to the nature of language: generative linguistics and general cognitive approaches. Some results of key acquisition studies from the two theoretical frameworks are discussed. From a generative perspective, constraints on wh-movement, feature geometry and syllable structure, and morphological development are highlighted. From a general cognitive point of view, the emergence of tense and aspect marking from a prototype account of inherent lexical aspect is reviewed. Reference is made to general cognitive learning theories and to sociocultural theory. The article also reviews individual differences research, specifically debate on the critical period in adult language acquisition, motivation, and memory. Finally, the article discusses the relationship between SLA research and second language pedagogy. Suggestions for further reading from recent handbooks on SLA are provided. WIREs Cogni Sci 2011 2 277-286 DOI: 10.1002/wcs.106 For further resources related to this article, please visit the WIREs website.

Second language acquisition.

The variable rule program is one of the predominant data analysis tools used in sociolinguistics, employed successfully for over three decades to quantitatively assess the influence of multiple factors on linguistic variables. However, its most popular current version, GoldVarb, lacks flexibility and also isolates its users from the wider community of quantitative linguists. A new version of the variable rule program, Rbrul, attempts to resolve these concerns, and with mixed-effects modelling also addresses a more serious problem whereby GoldVarb overestimates the significance of effects. Rbrul's superior performance is demonstrated on both simulated and real data sets.

/pdf/getting-off-the-goldvarb-standard-introducing-rbrul-for-4bfyzwwwe6.pdf

Getting off the GoldVarb standard:introducing Rbrul for mixed-effects variable rule analysis

Identifying a person by his or her voice is an important human trait most take for granted in natural human-to-human interaction/communication. Speaking to someone over the telephone usually begins by identifying who is speaking and, at least in cases of familiar speakers, a subjective verification by the listener that the identity is correct and the conversation can proceed. Automatic speaker-recognition systems have emerged as an important means of verifying identity in many e-commerce applications as well as in general business interactions, forensics, and law enforcement. Human experts trained in forensic speaker recognition can perform this task even better by examining a set of acoustic, prosodic, and linguistic characteristics of speech in a general approach referred to as structured listening. Techniques in forensic speaker recognition have been developed for many years by forensic speech scientists and linguists to help reduce any potential bias or preconceived understanding as to the validity of an unknown audio sample and a reference template from a potential suspect. Experienced researchers in signal processing and machine learning continue to develop automatic algorithms to effectively perform speaker recognition?with ever-improving performance?to the point where automatic systems start to perform on par with human listeners. In this article, we review the literature on speaker recognition by machines and humans, with an emphasis on prominent speaker-modeling techniques that have emerged in the last decade for automatic systems. We discuss different aspects of automatic systems, including voice-activity detection (VAD), features, speaker models, standard evaluation data sets, and performance metrics. Human speaker recognition is discussed in two parts?the first part involves forensic speaker-recognition methods, and the second illustrates how a na?ve listener performs this task from a neuroscience perspective. We conclude this review with a comparative study of human versus machine speaker recognition and attempt to point out strengths and weaknesses of each.

Speaker Recognition by Machines and Humans: A tutorial review

A series of experiments shows that Spanish learners of English acquire the ship-sheep contrast in a way specific to their target dialect (Scottish or Southern British English) and that many learners exhibit a perceptual strategy found in neither Spanish nor English. To account for these facts as well as for the findings of earlier research on second language (L2) speech perception, we provide an Optimality Theoretic model of phonological categorization that comes with a formal learning algorithm for its acquisition. Within this model, the dialect-dependent and L2-specific facts provide evidence for the hypotheses of Full Transfer and Full Access.We would like to thank the audiences of the 25th Penn Linguistics Colloquium (Philadelphia, 2001), Generative Approaches to Language Acquisition 2001 (Palmela), and EuroSLA 11 (Paderborn, 2001) and of talks in Lisbon, Amsterdam, Edinburgh, Utrecht, and Munich, all in 2001 as well, for their questions and remarks, and Rachel Hayes, Michael Sharwood Smith, and five anonymous SSLA reviewers for their helpful comments on earlier versions of this paper. This research was partially sponsored by grant 355-75-003 to Boersma from the Netherlands Organization for Scientific Research (NWO).

/pdf/bridging-the-gap-between-l2-speech-perception-research-and-2upisf9gis.pdf

Bridging the gap between l2 speech perception research and phonological theory

Measuring the validity and reliability of forensic likelihood-ratio systems

Logistic-regression calibration and fusion are potential steps in the calculation of forensic likelihood ratios. The present paper provides a tutorial on logistic-regression calibration and fusion at a practical conceptual level with minimal mathematical complexity. A score is log-likelihood-ratio like in that it indicates the degree of similarity of a pair of samples while taking into consideration their typicality with respect to a model of the relevant population. A higher-valued score provides more support for the same-origin hypothesis over the different-origin hypothesis than does a lower-valued score; however, the absolute values of scores are not interpretable as log likelihood ratios. Logistic-regression calibration is a procedure for converting scores to log likelihood ratios, and logistic-regression fusion is a procedure for converting parallel sets of scores from multiple forensic-comparison systems to log likelihood ratios. Logistic-regression calibration and fusion were developed for automat...

Tutorial on logistic-regression calibration and fusion:converting a score to a likelihood ratio

https://openresearch-repository.anu.edu.au/bitstream/1885/54292/2/01_Morrison_Forensic_voice_comparison_and_2009.pdf

Forensic voice comparison and the paradigm shift

A comparison of procedures for the calculation of forensic likelihood ratios from acoustic-phonetic data: Multivariate kernel density (MVKD) versus Gaussian mixture model-universal background model (GMM-UBM)

Non-contemporaneous speech samples from 27 male speakers of Australian English were compared in a forensic likelihood-ratio framework. Parametric curves (polynomials and discrete cosine transforms) were fitted to the formant trajectories of the diphthongs ∕aɪ∕, ∕eɪ∕, ∕oʊ∕, ∕aʊ∕, and ∕ɔɪ∕. The estimated coefficient values from the parametric curves were used as input to a generative multivariate-kernel-density formula for calculating likelihood ratios expressing the probability of obtaining the observed difference between two speech samples under the hypothesis that the samples were produced by the same speaker versus under the hypothesis that they were produced by different speakers. Cross-validated likelihood-ratio results from systems based on different parametric curves were calibrated and evaluated using the log-likelihood-ratio cost function (Cllr). The cross-validated likelihood ratios from the best-performing system for each vowel phoneme were fused using logistic regression. The resulting fused system had a very low error rate, thus meeting one of the requirements for admissibility in court.

Geoffrey Stewart Morrison

Papers

Measuring the validity and reliability of forensic likelihood-ratio systems

Tutorial on logistic-regression calibration and fusion:converting a score to a likelihood ratio

Forensic voice comparison and the paradigm shift

A comparison of procedures for the calculation of forensic likelihood ratios from acoustic-phonetic data: Multivariate kernel density (MVKD) versus Gaussian mixture model-universal background model (GMM-UBM)

Likelihood-ratio forensic voice comparison using parametric representations of the formant trajectories of diphthongs.