The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

https://faculty.wcas.northwestern.edu/ann-bradlow/publications/2008/Bradlow&Bent-Cognition08.pdf

Perceptual adaptation to non-native speech.

http://web.mit.edu/hst.722/www/Topics/Speech/Tourville_in%20press_NeuroImage_PrePrint.pdf

Neural mechanisms underlying auditory feedback control of speech.

Human beings have an amazing capacity to learn new skills and adapt to new environments However, several obstacles remain to be overcome in designing paradigms to broadly improve quality of life Arguably, the most notable impediment to this goal is that learning tends to be quite specific to the trained regimen and does not transfer to even qualitatively similar tasks This severely limits the potential benefits of learning to daily life This review discusses training regimens that lead to the acquisition of new knowledge and strategies that can be used flexibly across a range of tasks and contexts Possible characteristics of training regimens are proposed that may be responsible for augmented learning, including the manner in which task difficulty is progressed, the motivational state of the learner, and the type of feedback the training provides When maximally implemented in rehabilitative paradigms, these characteristics may greatly increase the efficacy of training

/pdf/exercising-your-brain-a-review-of-human-brain-plasticity-and-2cy0jyk5en.pdf

Exercising your brain: a review of human brain plasticity and training-induced learning.

This article presents a review of the effects of adverse conditions (ACs) on the perceptual, linguistic, cognitive, and neurophysiological mechanisms underlying speech recognition. The review starts with a classification of ACs based on their origin: Degradation at the source (production of a noncanonical signal), degradation during signal transmission (interfering signal or medium-induced impoverishment of the target signal), receiver limitations (peripheral, linguistic, cognitive). This is followed by a parallel, yet orthogonal classification of ACs based on the locus of their effect: Perceptual processes, mental representations, attention, and memory functions. We then review the added value that ACs provide for theories of speech recognition, with a focus on fundamental themes in psycholinguistics: Content and format of lexical representations, time-course of lexical access, word segmentation, feed-back in speech perception and recognition, lexicalsemantic integration, interface between the speech system and general cognition, neuroanatomical organisation of speech processing. We conclude by advocating an approach to speech recognition that includes rather than neutralises complex listening environments and individual differences.

/pdf/speech-recognition-in-adverse-conditions-a-review-1gdpt01sgx.pdf

Speech recognition in adverse conditions: A review

Some acoustic cues for the perceptual categorization of American English regional dialects

Previous research by speech scientists on the acoustic characteristics of American English vowel systems has typically focused on a single regional variety, despite decades of sociolinguistic research demonstrating the extent of regional phonological variation in the United States. In the present study, acoustic measures of duration and first and second formant frequencies were obtained from five repetitions of 11 different vowels produced by 48 talkers representing both genders and six regional varieties of American English. Results revealed consistent variation due to region of origin, particularly with respect to the production of low vowels and high back vowels. The Northern talkers produced shifted low vowels consistent with the Northern Cities Chain Shift, the Southern talkers produced fronted back vowels consistent with the Southern Vowel Shift, and the New England, Midland, and Western talkers produced the low back vowel merger. These findings indicate that the vowel systems of American English are better characterized in terms of the region of origin of the talkers than in terms of a single set of idealized acoustic-phonetic baselines of “General” American English and provide benchmark data for six regional varieties.

/pdf/acoustic-characteristics-of-the-vowel-systems-of-six-1ntayfi52p.pdf

Acoustic characteristics of the vowel systems of six regional varieties of American English

Two groups of listeners learned to categorize a set of unfamiliar talkers by dialect region using sentences selected from the TIMIT speech corpus. One group learned to categorize a single talker from each of six American English dialect regions. A second group learned to categorize three talkers from each dialect region. Following training, both groups were asked to categorize new talkers using the same categorization task. While the single-talker group was more accurate during initial training and test phases when familiar talkers produced the sentences, the three-talker group performed better on the generalization task with unfamiliar talkers. This cross-over effect in dialect categorization suggests that while talker variation during initial perceptual learning leads to more difficult learning of specific exemplars, exposure to intertalker variability facilitates robust perceptual learning and promotes better categorization performance of unfamiliar talkers. The results suggest that listeners encode and use acoustic-phonetic variability in speech to reliably perceive the dialect of unfamiliar talkers.

Effects of talker variability on perceptual learning of dialects.

Early linguistic experience has been shown to affect speech perception in a variety of ways. The present experiment investigated the effects of early linguistic experience on dialect perception. Two groups of participants listened to sentences read by talkers from six American English dialects and were asked to identify where they thought the talkers were from using a forced-choice categorization task. We found that "army brats," who had lived in at least three different states, performed better than "homebodies," who had lived only in Indiana, in terms of overall categorization accuracy. Army brats who had lived in a given region also categorized talkers from that region more accurately than army brats who had not lived there. Clustering analyses on the stimulus-response confusion matrices revealed significant differences in the perceptual similarity spaces for the two listener groups. These results suggest that early exposure to linguistic variation affects how well listeners can identify where unfamiliar talkers are from.

/pdf/homebodies-and-army-brats-some-effects-of-early-linguistic-13jzj8pvmf.pdf

Homebodies and army brats: Some effects of early linguistic experience and residential history on dialect categorization

Listeners can explicitly categorize unfamiliar talkers by regional dialect with above-chance performance under ideal listening conditions. However, the extent to which this important source of variation affects speech processing is largely unknown. In a series of four experiments, we examined the effects of dialect variation on speech intelligibility in noise and the effects of noise on perceptual dialect classification. Results revealed that, on the one hand, dialect-specific differences in speech intelligibility were more pronounced at harder signal-to-noise ratios, but were attenuated under more favorable listening conditions. Listener dialect did not interact with talker dialect; for all listeners, at a range of noise levels, the General American talkers were the most intelligible and the Mid-Atlantic talkers were the least intelligible. Dialect classification performance, on the other hand, was poor even with only moderate amounts of noise. These findings suggest that at moderate noise levels, listeners are able to adapt to dialect variation in the acoustic signal such that some cross-dialect intelligibility differences are neutralized, despite relatively poor explicit dialect classification performance. However, at more difficult noise levels, participants cannot effectively adapt to dialect variation in the acoustic signal and cross-dialect differences in intelligibility emerge for all listeners, regardless of their dialect.

/pdf/perception-of-dialect-variation-in-noise-intelligibility-and-3a69wcooej.pdf

Cynthia G. Clopper

Papers

Some acoustic cues for the perceptual categorization of American English regional dialects

Acoustic characteristics of the vowel systems of six regional varieties of American English

Effects of talker variability on perceptual learning of dialects.

Homebodies and army brats: Some effects of early linguistic experience and residential history on dialect categorization

Perception of Dialect Variation in Noise: Intelligibility and Classification: