In this paper, we present the English Dialects App (EDA) and the English Dialects App Corpus (EDAC). EDA is a free iOS and Android app, launched in January 2016 that features a dialect quiz and dialect recordings. For the quiz, users indicate which variants of 26 words they use and the application guesses their local dialect; for the recordings, users can record a short text. The result is EDAC which includes metadata on mobility, ethnicity, age, educational level, and gender. More than 47,000 users from across the UK have indicated dialect variants for these 26 words, and more than 3,500 users have provided audio recordings. Unavoidably, EDAC does not successfully reflect distributions of age, ethnicity, qualification levels, and other parameters found for the UK population given that smartphone-based research reaches a specific stratum of the population. Yet there are also clear benefits to the sampling strategy used – benefits and pitfalls are discussed in this article. Future analyses will provide the most comprehensive understanding of English regional dialect variation since the work of the traditional dialectologists. We showcase two such analyses in this article. EDAC should, we demonstrate, be of interest to researchers in dialectology but also in forensic phonetics

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.

Several western Swiss German dialects roughly grouped around the nation's capital Bern show /l/ > [u] vocalization in various contexts. The spatial boundaries of /l/-vocalization in Swiss German are suspected to have been expanding since being described in the Linguistic Atlas of German-Speaking Switzerland in the middle of the 20th century. The present study assesses the overall expansion of /l/-vocalization by means of a rapid anonymous survey in 20 urban regional centers situated just beyond the traditional boundaries of /l/-vocalization highlighted by the Atlas. Results show that the expansion of /l/-vocalization mainly progresses in southeasterly, southerly, and westerly directions, but with much less success to the north and northwest, where the equally influential dialectal areas of Basel and Zürich seem to exert opposing influences. Further analysis of the data indicates that somewhat differing constraint hierarchies are at work in the different places to which vocalization has diffused.

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.

Fundamental frequency (f0) is a highly speaker-specific feature. Consequently, practitioners often use f0 information in forensic casework. Current research principally examines the use of long-term f0 statistics such as f0 means and standard deviations for forensic voice comparison. The present study investigates how short-term f0 features such as measured by the Fujisaki intonation model capture speaker-individuality. Based on data of a homogeneous group of Zurich German speakers, we conducted an experiment on a large corpus of read speech and on a subset of sentences that included speaking style variability (spontaneous vs. read). The latter is characteristic of forensic casework. Speakers demonstrated high between-speaker variability and low within-speaker variability across the two speaking styles for a number of f0 features. Given this evidence of speaker-individuality, we discuss Fujisaki f0 features’ potential for forensic voice comparison.