most citedPinyin Regularization in Error Correction for Chinese Speech Recognition with Large Language Models

3 citations · 6 across the 5 of their papers we have counts for

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5 papers

eess.AS2026

Triage knowledge distillation for speaker verification

Ju-ho Kim, Youngmoon Jung, Joon-Young Yang +3

Deploying speaker verification on resource-constrained devices remains challenging due to the computational cost of high-capacity models; knowledge distillation (KD) offers a remed…

eess.AS2026

MATE: Matryoshka Audio-Text Embeddings for Open-Vocabulary Keyword Spotting

Youngmoon Jung, Myunghun Jung, Joon-Young Yang +3

Open-vocabulary keyword spotting (KWS) with text-based enrollment has emerged as a flexible alternative to fixed-phrase triggers. Prior utterance-level matching methods, from an em…

eess.AS2026

DAME: Duration-Aware Matryoshka Embedding for Duration-Robust Speaker Verification

Youngmoon Jung, Joon-Young Yang, Ju-ho Kim +3

Short-utterance speaker verification remains challenging due to limited speaker-discriminative cues in short speech segments. While existing methods focus on enhancing speaker enco…

cs.CL20243 cited

Pinyin Regularization in Error Correction for Chinese Speech Recognition with Large Language Models

Zhiyuan Tang, Dong Wang, Shen Huang +1

Recent studies have demonstrated the efficacy of large language models (LLMs) in error correction for automatic speech recognition (ASR). However, much of the research focuses on t…

cs.SD20243 cited

CTC-aligned Audio-Text Embedding for Streaming Open-vocabulary Keyword Spotting

Sichen Jin, Youngmoon Jung, Seungjin Lee +3

This paper introduces a novel approach for streaming openvocabulary keyword spotting (KWS) with text-based keyword enrollment. For every input frame, the proposed method finds the…