1 citations · 1 across the 8 of their papers we have counts for
8 papers
Phone Segmentation and Recognition through Phonological Activation Mapping
Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh +8
Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the re…
An Empirical Recipe for Universal Phone Recognition
Shikhar Bharadwaj, Chin-Jou Li, Kwanghee Choi +4
Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive. Highly performant English-focused models d…
Bagpiper: Solving Open-Ended Audio Tasks via Rich Captions
Jinchuan Tian, Haoran Wang, Bo-Hao Su +14
Current audio foundation models typically rely on rigid, task-specific supervision (e.g., speech recognition), addressing isolated factors of audio rather than the whole. In contra…
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…
Prompt-MII: Meta-Learning Instruction Induction for LLMs
Emily Xiao, Yixiao Zeng, Ada Chen +3
A popular method to adapt large language models (LLMs) to new tasks is in-context learning (ICL), which is effective but incurs high inference costs as context length grows. In thi…
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…
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…
Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse Attention
Emily Xiao, Chin-Jou Li, Yilin Zhang +2
Many-shot in-context learning has recently shown promise as an alternative to finetuning, with the major advantage that the same model can be served for multiple tasks. However, th…