Effects of mobility on dialect change: Introducing the linguistic mobility index.

Increased geographical mobility prompts dialectologists to factor in survey participants' exposure to linguistic variation in their research. Changing mobility patterns (e.g. longer-distance commuting; easier relocation to distant places for work, study or marriage) have caused linguistic connections to become much more diverse, potentially contributing to the acceleration of dialect change. In this methodological work we propose the Linguistic Mobility Index (LMI) to estimate long-term exposure to dialectal variation and thereby to provide a reference of "localness" about survey participants. Based on data about a survey participant's linguistic biography, an LMI may comprise combinations of influential agents and environments, such as the dialects of parents and long-term partners, the places where participants have lived and worked, and the participants' level of education. We encapsulate the linguistic effects of these agents based on linguistic differences, the intensity and importance of the relationship. We quantify the linguistic effects in three steps. 1) The linguistic effect of an agent is represented by a linguistic distance, 2) This linguistic distance is weighted based on the intensity of the participant's exposure to the agent, and 3) Further weighted according to the relationship embodied by the agent. LMI is conceptualised and evaluated based on 500 speakers from 125 localities in the Swiss German Dialects Across Time and Space (SDATS) corpus, and guidance is provided for establishing LMI in other linguistic studies. For the assessment of LMI's applicability to other studies, four LMI prototypes are constructed based on the SDATS corpus, employing different theoretical considerations and combinations of influential agents and environments to simulate the availability of biographical data in other studies. Using mixed-effects modelling, we evaluate the utility of the LMI prototypes as predictors of dialect change between historic and contemporary linguistic data of Swiss German. The LMI prototypes successfully show that higher exposure to dialectal variation contributes to more dialect change and that its effect is stronger than some sociodemographic variables that are often tested for affecting dialect change (e.g. sex and educational background).

Reduction of Survey Sites in Dialectology: A New Methodology Based on Clustering.

Many language change studies aim for a partial revisitation, i.e., selecting survey sites from previous dialect studies. The central issue of survey site reduction, however, has often been addressed only qualitatively. Cluster analysis offers an innovative means of identifying the most representative survey sites among a set of original survey sites. In this paper, we present a general methodology for finding representative sites for an intended study, potentially applicable to any collection of data about dialects or linguistic variation. We elaborate the quantitative steps of the proposed methodology in the context of the "Linguistic Atlas of Japan" (LAJ). Next, we demonstrate the full application of the methodology on the "Linguistic Atlas of German-speaking Switzerland" (Germ.: "Sprachatlas der Deutschen Schweiz"-SDS), with the explicit aim of selecting survey sites corresponding to the aims of the current project "Swiss German Dialects Across Time and Space" (SDATS), which revisits SDS 70 years later. We find that depending on the circumstances and requirements of a study, the proposed methodology, introducing cluster analysis into the survey site reduction process, allows for a greater objectivity in comparison to traditional approaches. We suggest, however, that the suitability of any set of candidate survey sites resulting from the proposed methodology be rigorously revised by experts due to potential incongruences, such as the overlap of objectives and variables across the original and intended studies and ongoing dialect change.

Corrigendum: Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation.

[This corrects the article DOI: 10.3389/frai.2020.565682.].

A typology of laterals in twelve English dialects.

Allophonic patterns of variation in English laterals have been well studied in phonetics and phonology for decades, but establishing broad generalizations across varieties has proven challenging. In this study, a typology of onset/coda lateral distinctions in English is advanced using crowdsourced recordings from 95 speakers across twelve dialects of Anglo (UK) English. Results confirm the existence of dialects with and without onset/coda distinctions, and conditional inference trees are used to identify three main patterns in the data: (1) clear onsets and dark codas; (2) intermediate/dark onsets and dark codas, but with a positional distinction intact; and (3) dark onsets and dark codas, with minimal or no distinctions between positions.

General Northern English. Exploring Regional Variation in the North of England With Machine Learning.

In this paper, we present a novel computational approach to the analysis of accent variation. The case study is dialect leveling in the North of England, manifested as reduction of accent variation across the North and emergence of General Northern English (GNE), a pan-regional standard accent associated with middle-class speakers. We investigated this instance of dialect leveling using random forest classification, with audio data from a crowd-sourced corpus of 105 urban, mostly highly-educated speakers from five northern UK cities: Leeds, Liverpool, Manchester, Newcastle upon Tyne, and Sheffield. We trained random forest models to identify individual northern cities from a sample of other northern accents, based on first two formant measurements of full vowel systems. We tested the models using unseen data. We relied on undersampling, bagging (bootstrap aggregation) and leave-one-out cross-validation to address some challenges associated with the data set, such as unbalanced data and relatively small sample size. The accuracy of classification provides us with a measure of relative similarity between different pairs of cities, while calculating conditional feature importance allows us to identify which input features (which vowels and which formants) have the largest influence in the prediction. We do find a considerable degree of leveling, especially between Manchester, Leeds and Sheffield, although some differences persist. The features that contribute to these differences most systematically are typically not the ones discussed in previous dialect descriptions. We propose that the most systematic regional features are also not salient, and as such, they serve as sociolinguistic regional indicators. We supplement the random forest results with a more traditional variationist description of by-city vowel systems, and we use both sources of evidence to inform a description of the vowels of General Northern English.

Using Crowd-Sourced Speech Data to Study Socially Constrained Variation in Nonmodal Phonation.

This study examines the status of nonmodal phonation (e.g. breathy and creaky voice) in British English using smartphone recordings from over 2,500 speakers. With this novel data collection method, it uncovers effects that have not been reported in past work, such as a relationship between speakers' education and their production of nonmodal phonation. The results also confirm that previous findings on nonmodal phonation, including the greater use of creaky voice by male speakers than female speakers, extend to a much larger and more diverse sample than has been considered previously. This confirmation supports the validity of using crowd-sourced data for phonetic analyses. The acoustic correlates that were examined include fundamental frequency, H1*-H2*, cepstral peak prominence, and harmonic-to-noise ratio.

Analyzing linguistic variation and change using gamification web apps: The case of German-speaking Europe.

Research on regional linguistic variation typically involves data collection in the field. This process itself can take up several months if not years. In the present study we demonstrate how we can use web interactives in collaboration with media outlets for a fast gathering of regional, sociolinguistic data. In collaboration with SPIEGEL ONLINE and Tagesanzeiger, we developed a web interactive that predicts users' regional backgrounds from within German-speaking Europe. More than 1.9M people have participated in the interactive, more than 770K users have provided metadata. Said metadata allowed us to capture regional variation in language as of today, which we can compare to historical survey data-enabling us to track the evolution of German in Europe over the past 40 years. We report on regional levelling of lexical variants, a process which appears to be particularly prevalent in the northern parts of German-speaking Europe. We further found an effect of (former) national and regional borders on language use. This innovative paradigm allows us to collect sociolinguistic data of an unprecedented scale-at the same time it presents significant challenges, both of which-benefits and challenges-will be discussed in this contribution.

The size of the tongue movement area affects the temporal coordination of consonants and vowels-A proof of concept on investigating speech rhythm.

Recent research has revealed substantial between-speaker variation in speech rhythm, which in effect refers to the coordination of consonants and vowels over time. In the current proof-of-concept study, the hypothesis was investigated that these idiosyncrasies arise, in part, from differences in the tongue's movement amplitude. Speech rhythm was parameterized by means of the percentage over which speech is vocalic (%V) in the German pronoun "sie" [ziː]. The findings support the hypothesis: all else being equal, idiosyncratic %V values behaved proportionally to a speaker's tongue movement area. This research underlines the importance of studying language-external factors, such as a speaker's individual tongue movement behavior, to investigate variation in temporal coordination.

Crowdsourcing Language Change with Smartphone Applications.

Crowdsourcing linguistic phenomena with smartphone applications is relatively new. In linguistics, apps have predominantly been developed to create pronunciation dictionaries, to train acoustic models, and to archive endangered languages. This paper presents the first account of how apps can be used to collect data suitable for documenting language change: we created an app, Dialäkt Äpp (DÄ), which predicts users' dialects. For 16 linguistic variables, users select a dialectal variant from a drop-down menu. DÄ then geographically locates the user's dialect by suggesting a list of communes where dialect variants most similar to their choices are used. Underlying this prediction are 16 maps from the historical Linguistic Atlas of German-speaking Switzerland, which documents the linguistic situation around 1950. Where users disagree with the prediction, they can indicate what they consider to be their dialect's location. With this information, the 16 variables can be assessed for language change. Thanks to the playfulness of its functionality, DÄ has reached many users; our linguistic analyses are based on data from nearly 60,000 speakers. Results reveal a relative stability for phonetic variables, while lexical and morphological variables seem more prone to change. Crowdsourcing large amounts of dialect data with smartphone apps has the potential to complement existing data collection techniques and to provide evidence that traditional methods cannot, with normal resources, hope to gather. Nonetheless, it is important to emphasize a range of methodological caveats, including sparse knowledge of users' linguistic backgrounds (users only indicate age, sex) and users' self-declaration of their dialect. These are discussed and evaluated in detail here. Findings remain intriguing nevertheless: as a means of quality control, we report that traditional dialectological methods have revealed trends similar to those found by the app. This underlines the validity of the crowdsourcing method. We are presently extending DÄ architecture to other languages.

Rhythmic variability between speakers: articulatory, prosodic, and linguistic factors.

Between-speaker variability of acoustically measurable speech rhythm [%V, ΔV(ln), ΔC(ln), and Δpeak(ln)] was investigated when within-speaker variability of (a) articulation rate and (b) linguistic structural characteristics was introduced. To study (a), 12 speakers of Standard German read seven lexically identical sentences under five different intended tempo conditions (very slow, slow, normal, fast, very fast). To study (b), 16 speakers of Zurich Swiss German produced 16 spontaneous utterances each (256 in total) for which transcripts were made and then read by all speakers (4096 sentences; 16 speaker × 256 sentences). Between-speaker variability was tested using analysis of variance with repeated measures on within-speaker factors. Results revealed strong and consistent between-speaker variability while within-speaker variability as a function of articulation rate and linguistic characteristics was typically not significant. It was concluded that between-speaker variability of acoustically measurable speech rhythm is strong and robust against various sources of within-speaker variability. Idiosyncratic articulatory movements were found to be the most plausible factor explaining between-speaker differences.

Speaker-individuality in suprasegmental temporal features: Implications for forensic voice comparison.

Everyday experience tells us that it is often possible to identify a familiar speaker solely by his/her voice. Such observations reveal that speakers carry individual features in their voices. The present study examines how suprasegmental temporal features contribute to speaker-individuality. Based on data of a homogeneous group of Zurich German speakers, we conducted an experiment that included speaking style variability (spontaneous vs. read speech) and channel variability (high-quality vs. mobile phone-transmitted speech), both of which are characteristic of forensic casework. Speakers demonstrated high between-speaker variability in both read and spontaneous speech, and low within-speaker variability across the two speaking styles. Results further revealed that distortions of the type introduced by mobile telephony had little effect on suprasegmental temporal characteristics. Given this evidence of speaker-individuality, we discuss suprasegmental temporal features' potential for forensic voice comparison.