The diffusion of /l/-vocalization in Swiss German

Leemann, Adrian; Kolly, Marie-José; Werlen, Iwar; Britain, David; Studer-Joho, Dieter

doi:10.1017/s0954394514000076

Cited by 10 publications

(10 citation statements)

References 31 publications

(67 reference statements)

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“…In order to scrutinize the validity of the DÄ corpus, therefore, we decided to examine one particular linguistic variable that has received recent and thorough geolinguistic investigation in Swiss German—the spread of /l/ vocalization, as described in 3.3.2. By means of a well-established dialectological method—a rapid anonymous survey–[ 32 ] found a highly similar diffusion of /l/-vocalization in Swiss German to the one presented here. [ 32 ] examined 35 native dialect speakers on average (SD = 9) in each of 20 localities (i.e., nearly 700 subjects).…”

Section: Quality Control and Validating The App Resultssupporting

confidence: 64%

“…By means of a well-established dialectological method—a rapid anonymous survey–[ 32 ] found a highly similar diffusion of /l/-vocalization in Swiss German to the one presented here. [ 32 ] examined 35 native dialect speakers on average (SD = 9) in each of 20 localities (i.e., nearly 700 subjects). They reported diffusion of this typically Bernese German phenomenon towards the west, south, and southeast.…”

Section: Quality Control and Validating The App Resultssupporting

confidence: 64%

“…/l/ can be pronounced as: [l], [lː], [ʊː], [ɬ], [ɬː]. The realization of /l/ as [ʊ], or l-vocalization, has been reported to be diffusing in Swiss German [ 31 , 32 ]. Fig 11 shows the distribution of this vocalized variant, [ˈxæʊːə], as represented in the contemporary DÄ corpus.…”

Section: Resultsmentioning

confidence: 99%

“…In Adelboden (red ‘x’ symbol in Fig 15 ), for example, the traditional method documented that 11% of speakers vocalized; in the crowdsourcing method, 7% of the speakers reported vocalization. Not all localities from the [ 32 ] study are represented in the Atlas (Baden and Interlaken), which is why the closest Atlas neighbor was taken as a reference point; for Baden this was Birmenstorf, and for Interlaken this was Unterseen. The red line indicates the regression line, with the red area denoting the 95% confidence limits.…”

Section: Quality Control and Validating The App Resultsmentioning

confidence: 99%

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Crowdsourcing Language Change with Smartphone Applications

et al. 2016

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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.

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Section: Quality Control and Validating The App Resultssupporting

confidence: 64%

Section: Quality Control and Validating The App Resultssupporting

confidence: 64%

Section: Resultsmentioning

confidence: 99%

Section: Quality Control and Validating The App Resultsmentioning

confidence: 99%

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Crowdsourcing Language Change with Smartphone Applications

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“…Johnson and Britain (2007) note that /l/-vocalization is known to have emerged in those varieties of English that already have a dark /l/, and it is not attested in varieties such as Irish English, where /l/ is relatively lighter. Apart from English, diachronic vocalization of dark /l/ has been reported for Catalan, Provençal, and Italian dialects (Recasens, 1996), Occitan (Müller, 2011), Dutch (Jongkind & van Reenen, 2007), Swiss German (Leemann, Kolly, Werlen, Britain, & Studer-Joho, 2014), and Polish (Koneczna, 1965;Nagórko, 1996), inter alia.…”

Section: Darkening As a Precursor To Vocalizationmentioning

confidence: 99%

Locating de-lateralization in the pathway of sound changes affecting coda /l/

Strycharczuk

Derrick

Shaw

2020

Laboratory Phonology: Journal of the Association for Laboratory Phonology

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'Vocalization' is a label commonly used to describe an ongoing change in progress affecting coda /l/ in multiple accents of English. The label is directly linked to the loss of consonantal constriction observed in this process, but it also implicitly signals a specific type of change affecting manner of articulation from consonant to vowel, which involves loss of tongue lateralization, the defining property of lateral sounds. In this study, we consider two potential diachronic pathways of change: an abrupt loss of lateralization which follows from the loss of apical constriction, versus slower gradual loss of lateralization that tracks the articulatory changes to the dorsal component of /l/. We present articulatory data from seven speakers of New Zealand English, acquired using a combination of midsagittal and lateral EMA, as well as midsagittal ultrasound. Different stages of sound change are reconstructed through synchronic variation between light, dark, and vocalized /l/, induced by systematic manipulation of the segmental and morphosyntactic environment, and complemented by comparison of different individual articulatory strategies. Our data show a systematic reduction in lateralization that is conditioned by increasing degrees of /l/-darkening and /l/-vocalization. This observation supports the idea of a gradual diachronic shift and the following pathway of change: /l/-darkening, driven by the dorsal gesture, precipitates some loss of lateralization, which is followed by loss of the apical gesture. This pathway indicates that loss of lateralization is an integral component in the changes in manner of articulation of /l/ from consonantal to vocalic.

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Speaker-invariant suprasegmental temporal features in normal and disguised speech

Leemann

Kolly

2015

Speech Communication

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Introduction Voice disguise refers to the voluntary act of changing one"s voice as a means to mask identity. In some forensic cases, perpetrators disguise their voices. The German Federal Police Office (BKA) reports that cases involving voice disguise range between 12.6% (Wagner, 1998) and 22.9% (Braun, 2009). Current research distinguishes between two types of voice disguise: electronic voice disguise, e.g. a perpetrator applies an electronic voice changer, and non-electronic voice disguise (Masthoff, 1996; Künzel, 2000; Perrot et al., 2007). Existing research on the former type recognizes the critical role of electronic voice disguise on voice identification: an artificial change in voice fundamental frequency, for example, negatively affects identification performance (Clark and Foulkes, 2007). Non-electronic disguise, however, occurs more frequently and can typically be broken up into (a) a change in voice source characteristics (e.g. raised pitch, lowered pitch, whispered speech), (b) a change in resonance features (e.g. foreign object in vocal tract, hypo-/hypernasality, tissue held to mouth), (c) a change in language, accent, and dialect (Künzel, 2000; Perrot et al., 2007; Neuhauser, 2011a). Previous research has shown that voice disguise can lead to high withinspeaker variability, affecting various acoustic parameters. Hollien and Majewksi (1977) reported high within-speaker variability in long-term spectra under various voice disguise conditions (disguise of the speaker"s own choice). Face covering as a means of voice disguise has been reported to affect spectral characteristics of fricatives (Fecher and Watt, 2011) and the amplitude of the overall signal (Llamas et al., 2009). Hirson and Duckworth (1993) showed that the intentional application of creaky voice as a disguise affects spectral characteristics of /s/. A further strategy for disguising one"s voice is to imitate another speaker. Eriksson and Wretling (1997) and Endres et al. (1971) reported that speakers particularly aim at matching f0 and formant frequencies when asked to imitate another speaker. Most often, this f0 and formant matching is successful. High within-speaker variability-such authentic or disguised voices-makes it difficult for forensic practitioners to draw conclusions about the speakers" identity. It is thus desirable in forensic cases that the acoustic features examined reveal maximum between-speaker and minimum within-speaker variability (Nolan, 2009). How suprasegmental temporal features behave in voice-disguised speech is currently unknown. The objective of this contribution is to test if and how temporal features that are not restricted to a single segment but rather to the more global temporal organization of speech remain speaker-invariant in one form of disguised speech, namely in cases where a different dialect is imitated. Such features have shown promising trends in this respect: preliminary research in the context of voice disguise has shown that suprasegmental temporal features such as the amount of voicing in the s...

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The diffusion of /l/-vocalization in Swiss German

Cited by 10 publications

References 31 publications

Crowdsourcing Language Change with Smartphone Applications

Crowdsourcing Language Change with Smartphone Applications

Locating de-lateralization in the pathway of sound changes affecting coda /l/

Speaker-invariant suprasegmental temporal features in normal and disguised speech

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