1 citations · 1 across the 4 of their papers we have counts for
8 papers
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…
Adversarial Deep Metric Learning for Cross-Modal Audio-Text Alignment in Open-Vocabulary Keyword Spotting
Youngmoon Jung, Yong-Hyeok Lee, Myunghun Jung +3
For text enrollment-based open-vocabulary keyword spotting (KWS), acoustic and text embeddings are typically compared at either the phoneme or utterance level. To facilitate this,…
Text-Aware Adapter for Few-Shot Keyword Spotting
Youngmoon Jung, Jinyoung Lee, Seungjin Lee +3
Recent advances in flexible keyword spotting (KWS) with text enrollment allow users to personalize keywords without uttering them during enrollment. However, there is still room fo…
Dynamic Noise Embedding: Noise Aware Training and Adaptation for Speech Enhancement
Joohyung Lee, Youngmoon Jung, Myunghun Jung +1
Estimating noise information exactly is crucial for noise aware training in speech applications including speech enhancement (SE) which is our focus in this paper. To estimate nois…
Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification using CTC-based Soft VAD and Global Query Attention
Myunghun Jung, Youngmoon Jung, Jahyun Goo +1
Keyword spotting (KWS) and speaker verification (SV) have been studied independently although it is known that acoustic and speaker domains are complementary. In this paper, we pro…
Improving Multi-Scale Aggregation Using Feature Pyramid Module for Robust Speaker Verification of Variable-Duration Utterances
Youngmoon Jung, Seong Min Kye, Yeunju Choi +2
Currently, the most widely used approach for speaker verification is the deep speaker embedding learning. In this approach, we obtain a speaker embedding vector by pooling single-s…