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Found 5 papers, 2 papers with code
ASR for Non-standardised Languages with Dialectal Variation: the case of Swiss German
A formal comparison shows that the system trained on the normalised transcriptions achieves better results in word error rate (WER) (29. 39%) but underperforms at the character level, suggesting dialectal transcriptions offer a viable solution for downstream applications where dialectal differences are important.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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TeDDi Sample: Text Data Diversity Sample for Language Comparison and Multilingual NLP
We present the TeDDi sample, a diversity sample of text data for language comparison and multilingual Natural Language Processing.
A Measure for Transparent Comparison of Linguistic Diversity in Multilingual NLP Data Sets
Typologically diverse benchmarks are increasingly created to track the progress achieved in multilingual NLP.
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level Rules
We apply our methodology to analyze the model{'}s decisions on three typologically-different languages and find that a) our pattern extraction method applied to cross-attention weights uncovers variation in form of inflection morphemes, b) pattern extraction from self-attention shows triggers for such variation, c) both types of patterns are closely aligned with grammar inflection classes and class assignment criteria, for all three languages.
From characters to words: the turning point of BPE merges
The distributions of orthographic word types are very different across languages due to typological characteristics, different writing traditions and potentially other factors.
