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Between-speaker variability of acoustically measurable speech rhythm (%V, ∆V(ln), ∆C(ln), and ∆Peak(ln)) and speech rate (rateCV) 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 7 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 x 256 sentences). Between-speaker variability was tested using ANOVA 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. We 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 likely factor explaining between-speaker differences.

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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.

Linguistic data collection typically involves conducting interviews with participants in close proximity. The safety precautions related to the COVID-19 pandemic brought such data collection to an abrupt halt: Social distancing forced linguistic fieldwork into involuntary hibernation in many parts of the world. Such hardship, however, can inspire innovation. In this contribution, we present an approach that – we believe – enables a reliable switch from in-person, face-to-face interviews to virtual, online data collection. In this approach, participants remain at home and navigate a smartphone application, enabling high-quality audio recordings and multisensory presentation of linguistic material, while they are being supervised via videoconferencing (Zoom 2020https://zoom.us/ (accessed 11 August 2020)). The smartphone app and the infrastructure presented are open source, accessible, and adaptable to researchers’ specific needs. To explore whether participants’ experiences of in-person data collection are different from participation in a virtual setting, we conducted a study with 36 participants. Overall, findings revealed a substantial degree of overlap in interview experience, setting a methodological baseline for future work. We end this contribution by discussing the benefits and pitfalls of this new approach.