Usage-based analyses of teacher corpora and code-switching (Boztepe, 2003) are an important next stage in understanding language acquisition. Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify. Using quantitative methods to understand language learning and teaching is difficult work as the â€˜transcription bottleneckâ€™ constrains the size of datasets. We found that using an automatic speech recognition (ASR) toolkit with a small set of training data is likely to speed data collection in this context (Maxwelll-Smith et al., 2020).
|Publication status||Published - 2021|
|Event||Computational Approaches to Linguistic Code-Switching - Mexico City|
Duration: 1 Jan 2021 → …
|Conference||Computational Approaches to Linguistic Code-Switching|
|Period||1/01/21 → …|