NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset.

NLTK: The Natural Language Toolkit

The Natural Language Toolkit is a suite of program modules, data sets and tutorials supporting research and teaching in computational linguistics and natural language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past year the toolkit has been rewritten, simplifying many linguistic data structures and taking advantage of recent enhancements in the Python language. This paper reports on the simplified toolkit and explains how it is used in teaching NLP.

/pdf/nltk-the-natural-language-toolkit-36bbn3dh0s.pdf

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

http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

The objective of this paper is speaker recognition under noisy and unconstrained conditions. 
We make two key contributions. First, we introduce a very large-scale audio-visual speaker recognition dataset collected from open-source media. Using a fully automated pipeline, we curate VoxCeleb2 which contains over a million utterances from over 6,000 speakers. This is several times larger than any publicly available speaker recognition dataset. 
Second, we develop and compare Convolutional Neural Network (CNN) models and training strategies that can effectively recognise identities from voice under various conditions. The models trained on the VoxCeleb2 dataset surpass the performance of previous works on a benchmark dataset by a significant margin.

VoxCeleb2: Deep Speaker Recognition.

The authors present a model of the phonotactic and orthographic constraints of Australian and Standard Southern British English monosyllables. This model is used as the basis for a web-based psycholinguistic resource, the ARC Nonword Database, which contains 358,534 monosyllabic nonwords—48,534 pseudohomophones and 310,000 non-pseudohomophonic nonwords. Items can be selected from the ARC Nonword Database on the basis of a wide variety of properties known or suspected to be of theoretical importance for the investigation of reading.

358,534 nonwords: The ARC Nonword Database

The focus of this paper is an acoustic analysis of citation‐form monophthongs and diphthongs produced by a large number of male and female talkers whose accents vary from broad to general to cultivated and who were recorded as part of the Australian National Database of Spoken Language (ANDOSL). Following an initial auditory categorisation of the talkers’ accents, the formants frequencies were calculated and the data were labelled for vowel target positions. Four main kinds of analysis were carried out: of monophthongs, of onglides in /i u/ vowels, of the trajectories in rising diphthongs, and of the trajectories of falling diphthongs. Consistently with earlier studies, the results show that the broad/general/cultivated accent differences are confined mostly to the rising diphthongs and to ongliding in /i/. The production of the falling diphthongs was found to be phonetically the most variable of all vowel categories. Some proposals are included for a modification to the transcription system of A...

An acoustic phonetic study of broad, general, and cultivated Australian English vowels*

http://www.phonetik.uni-muenchen.de/~jmh/research/papers/cassidy01.specom.pdf

Multi-level annotation in the Emu speech database management system

Preface. Vowel and Consonant Transcriptions. Contents of the CD-Rom. 1. The Scope of Speech Acoustics. 2. The Physics of Speech. 3. The Acoustic Theory of Speech Production. 4. Segmental and Prosodic Cues. 5. Time-Domain Analysis. 6. Frequency-Domain Analysis. 7. Digital Formant Synthesis. 8. Linear Prediction of Speech. 9. Classification of Speech Data. References.

Techniques in Speech Acoustics

The aim of the study was to establish whether /u/-fronting, a sound change in progress in standard southern British, could be linked synchronically to the fronting effects of a preceding anterior consonant both in speech production and speech perception. For the production study, which consisted of acoustic analyses of isolated monosyllables produced by two different age groups, it was shown for younger speakers that /u/ was phonetically fronted and that the coarticulatory influence of consonants on /u/ was less than in older speakers. For the perception study, responses were elicited from the same subjects to two minimal word-pair continua that differed in the direction of the consonants’ coarticulatory fronting effects on /u/. Consistent with their speech production, young listeners’ /u/ category boundary was shifted toward /i/ and they compensated perceptually less for the fronting effects of the consonants on /u/ than older listeners. The findings support Ohala’s model in which certain sound changes can be linked to the listener’s failure to compensate for coarticulation. The results are also shown to be consistent with episodic models of speech perception in which phonological frequency effects bring about a realignment of the variants of a phonological category in speech production and perception.

/pdf/compensation-for-coarticulation-u-fronting-and-sound-change-2uj7xzzids.pdf

Jonathan Harrington

Papers

358,534 nonwords: The ARC Nonword Database

An acoustic phonetic study of broad, general, and cultivated Australian English vowels*

Multi-level annotation in the Emu speech database management system

Techniques in Speech Acoustics

Compensation for coarticulation, /u/-fronting, and sound change in standard southern British: an acoustic and perceptual study.