Search Results for author:
Found 8 papers, 7 papers with code
WikiHan: A New Comparative Dataset for Chinese Languages
Most comparative datasets of Chinese varieties are not digital; however, Wiktionary includes a wealth of transcriptions of words from these varieties.
Self-supervised Speech Representations Still Struggle with African American Vernacular English
Underperformance of ASR systems for speakers of African American Vernacular English (AAVE) and other marginalized language varieties is a well-documented phenomenon, and one that reinforces the stigmatization of these varieties.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Can Large Language Models Code Like a Linguist?: A Case Study in Low Resource Sound Law Induction
Historical linguists have long written a kind of incompletely formalized ''program'' that converts reconstructed words in an ancestor language into words in one of its attested descendants that consist of a series of ordered string rewrite functions (called sound laws).
Phonotactic Complexity across Dialects
Received wisdom in linguistic typology holds that if the structure of a language becomes more complex in one dimension, it will simplify in another, building on the assumption that all languages are equally complex (Joseph and Newmeyer, 2012).
Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study
We describe a set of new methods to partially automate linguistic phylogenetic inference given (1) cognate sets with their respective protoforms and sound laws, (2) a mapping from phones to their articulatory features and (3) a typological database of sound changes.
Evaluating Self-supervised Speech Models on a Taiwanese Hokkien Corpus
Taiwanese Hokkien is declining in use and status due to a language shift towards Mandarin in Taiwan.
Transformed Protoform Reconstruction
We update their model with the state-of-the-art seq2seq model: the Transformer.
PWESuite: Phonetic Word Embeddings and Tasks They Facilitate
Mapping words into a fixed-dimensional vector space is the backbone of modern NLP.
