10 citations · 54 across the 50 of their papers we have counts for
7 papers · 2 filters
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…