10 citations · 54 across the 50 of their papers we have counts for
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Multilingual Dysarthric Speech Assessment Using Universal Phone Recognition and Language-Specific Phonemic Contrast Modeling
Eunjung Yeo, Julie M. Liss, Visar Berisha +1
The growing prevalence of neurological disorders associated with dysarthria motivates the need for automated intelligibility assessment methods that are applicalbe across languages…
Linear Script Representations in Speech Foundation Models Enable Zero-Shot Transliteration
Ryan Soh-Eun Shim, Kwanghee Choi, Kalvin Chang +8
Multilingual speech foundation models such as Whisper are trained on web-scale data, where data for each language consists of a myriad of regional varieties. However, different reg…
PRiSM: Benchmarking Phone Realization in Speech Models
Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim +13
Phone recognition (PR) serves as the atomic interface for language-agnostic modeling for cross-lingual speech processing and phonetic analysis. Despite prolonged efforts in develop…
POWSM: A Phonetic Open Whisper-Style Speech Foundation Model
Chin-Jou Li, Kalvin Chang, Shikhar Bharadwaj +5
Recent advances in spoken language processing have led to substantial progress in phonetic tasks such as automatic speech recognition (ASR), phone recognition (PR), grapheme-to-pho…
ZIPA: A family of efficient models for multilingual phone recognition
Jian Zhu, Farhan Samir, Eleanor Chodroff +1
We present ZIPA, a family of efficient speech models that advances the state-of-the-art performance of crosslinguistic phone recognition. We first curated IPAPack++, a large-scale…
PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics
Atharva Naik, Prakam, Yash Mathur +6
Although many benchmarks evaluate the reasoning abilities of Large Language Models (LLMs) within domains such as mathematics, coding, or data wrangling, few abstract away from doma…
Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages
Chin-Jou Li, Eunjung Yeo, Kwanghee Choi +7
Automatic speech recognition (ASR) for dysarthric speech remains challenging due to data scarcity, particularly in non-English languages. To address this, we fine-tune a voice conv…
Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment
Kwanghee Choi, Eunjung Yeo, Kalvin Chang +2
Allophony refers to the variation in the phonetic realization of a phoneme based on its phonetic environment. Modeling allophones is crucial for atypical pronunciation assessment,…
Cross-Lingual IPA Contrastive Learning for Zero-Shot NER
Jimin Sohn, David R. Mortensen
Existing approaches to zero-shot Named Entity Recognition (NER) for low-resource languages have primarily relied on machine translation, whereas more recent methods have shifted fo…
DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Models
Niyati Bafna, Emily Chang, Nathaniel R. Robinson +4
Most of the world's languages and dialects are low-resource, and lack support in mainstream machine translation (MT) models. However, many of them have a closely-related high-resou…
Derivational Morphology Reveals Analogical Generalization in Large Language Models
Valentin Hofmann, Leonie Weissweiler, David Mortensen +2
What mechanisms underlie linguistic generalization in large language models (LLMs)? This question has attracted considerable attention, with most studies analyzing the extent to wh…
Self-supervised Speech Representations Still Struggle with African American Vernacular English
Kalvin Chang, Yi-Hui Chou, Jiatong Shi +4
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 r…
Can Large Language Models Code Like a Linguist?: A Case Study in Low Resource Sound Law Induction
Atharva Naik, Kexun Zhang, Nathaniel Robinson +7
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 des…
Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages
Jimin Sohn, Haeji Jung, Alex Cheng +3
Existing zero-shot cross-lingual NER approaches require substantial prior knowledge of the target language, which is impractical for low-resource languages. In this paper, we propo…
Semisupervised Neural Proto-Language Reconstruction
Liang Lu, Peirong Xie, David R. Mortensen
Existing work implementing comparative reconstruction of ancestral languages (proto-languages) has usually required full supervision. However, historical reconstruction models are…
Neural Proto-Language Reconstruction
Chenxuan Cui, Ying Chen, Qinxin Wang +1
Proto-form reconstruction has been a painstaking process for linguists. Recently, computational models such as RNN and Transformers have been proposed to automate this process. We…
Improved Neural Protoform Reconstruction via Reflex Prediction
Liang Lu, Jingzhi Wang, David R. Mortensen
Protolanguage reconstruction is central to historical linguistics. The comparative method, one of the most influential theoretical and methodological frameworks in the history of t…
Verbing Weirds Language (Models): Evaluation of English Zero-Derivation in Five LLMs
David R. Mortensen, Valentina Izrailevitch, Yunze Xiao +2
Lexical-syntactic flexibility, in the form of conversion (or zero-derivation) is a hallmark of English morphology. In conversion, a word with one part of speech is placed in a non-…
Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons
Shijia Zhou, Leonie Weissweiler, Taiqi He +3
In this paper, we make a contribution that can be understood from two perspectives: from an NLP perspective, we introduce a small challenge dataset for NLI with large lexical overl…
Wav2Gloss: Generating Interlinear Glossed Text from Speech
Taiqi He, Kwanghee Choi, Lindia Tjuatja +6
Thousands of the world's languages are in danger of extinction--a tremendous threat to cultural identities and human language diversity. Interlinear Glossed Text (IGT) is a form of…
Phonotactic Complexity across Dialects
Ryan Soh-Eun Shim, Kalvin Chang, David R. Mortensen
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…
Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study
Kalvin Chang, Nathaniel R. Robinson, Anna Cai +3
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…
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model
Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban +10
Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills. However, there have been relative…
ChatGPT MT: Competitive for High- (but not Low-) Resource Languages
Nathaniel R. Robinson, Perez Ogayo, David R. Mortensen +1
Large language models (LLMs) implicitly learn to perform a range of language tasks, including machine translation (MT). Previous studies explore aspects of LLMs' MT capabilities. H…