4 papers
MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition
Sangmin Lee, Woojin Chung, Woongjib Choi +1
Massively multilingual automatic speech recognition (ASR) models covering hundreds of languages must maintain robust performance across diverse linguistic and acoustic conditions.…
LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration
Sangmin Lee, Woo-Jin Chung, Hong-Goo Kang
Building a universal multilingual automatic speech recognition (ASR) model that performs equitably across languages has long been a challenge due to its inherent difficulties. To a…
UniCoM: A Universal Code-Switching Speech Generator
Sangmin Lee, Woojin Chung, Seyun Um +1
Code-switching (CS), the alternation between two or more languages within a single speaker's utterances, is common in real-world conversations and poses significant challenges for…
StableQuant: Layer Adaptive Post-Training Quantization for Speech Foundation Models
Yeona Hong, Hyewon Han, Woo-jin Chung +1
In this paper, we propose StableQuant, a novel adaptive post-training quantization (PTQ) algorithm for widely used speech foundation models (SFMs). While PTQ has been successfully…