To what extent do native speakers change their linguistic behavior when communicating with non-native speakers?
For our current project we investigate different aspects of non-native addressee register (NNAR). Previous studies have shown several effects of speaking with a non-native interlocutor including speaking more loudly and slowly, hyperarticulating, using more restricted vocabulary, and employing less complex syntactic structures. Our research will expand upon previous studies in two important ways: 1) exploring NNAR for non-native speakers with varying levels of proficiency and 2) analyzing not just one, but multiple linguistic levels. Further, we are interested in the degree of automaticity and awareness of register shifts.
We will elicit this data in a hypothesis-led way and build a spoken corpus with several levels of transcription and annotation. We are conducting an experiment in two counterbalanced sessions. In the first session a native German speaker is recorded acoustically when solving three tasks with an instructed native interlocutor. In the second session the German speaker is recorded engaging the same tasks but this time with a non-native speaker with either mid or high proficiency in German.
The three tasks focus on different aspects of natural language data collection. The first task is a ‘spot the difference’ task, known as a diapix. Solving a diapix task ensures a balanced speech contribution and allows us to quantify the efficiency of the communication strategy by counting the differences found by each speaker. Diapix tasks also enable us to reliably channel participants towards the production of specific lexical and phonetic features. In contrast, we can obtain data under more natural conditions from a free conversation, which is the second task. The third task is a picture description consisting of one scripted description read by the participant and one controlled task for which the participant describes the picture in their own words. This task is designed to test whether the native speaker’s intelligibility increases by switching to NNAR.
Having collected the audio data, we will transliterate the dialogues using Praat. Next we will use WebMAUS to generate annotations on the phonetic segmental level (esp. corner vowels). The transcription and audio files will then be used for two kinds of corpora: an EMU database, which is well suited for processing acoustic data, and an ANNIS corpus because the ANNIS search tool is optimized for deeply annotated text-based corpora. We will then apply multilevel and multivariate statistics. For phonetic analyses we will extract the formant frequencies of the tense vowels in the stressed syllables of content words. Under the hypothesis that NNAR may be less lexically complex, we will measure lexical density and lexical diversity as well as morphological and syntactic complexity, based on complexity measures developed for SLA research. Finally we will conduct a listening experiment to evaluate whether the NNAR shifts made by participants improve the comprehension of the targeted non-native speaker.
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1412, 416591334.
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