25 citations · 49 across the 17 of their papers we have counts for
8 papers · 1 filter
SNAC: Speaker-normalized affine coupling layer in flow-based architecture for zero-shot multi-speaker text-to-speech
Byoung Jin Choi, Myeonghun Jeong, Joun Yeop Lee +1
Zero-shot multi-speaker text-to-speech (ZSM-TTS) models aim to generate a speech sample with the voice characteristic of an unseen speaker. The main challenge of ZSM-TTS is to incr…
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition
Ji Won Yoon, Beom Jun Woo, Sunghwan Ahn +2
Recently, the advance in deep learning has brought a considerable improvement in the end-to-end speech recognition field, simplifying the traditional pipeline while producing promi…
Adversarial Speaker-Consistency Learning Using Untranscribed Speech Data for Zero-Shot Multi-Speaker Text-to-Speech
Byoung Jin Choi, Myeonghun Jeong, Minchan Kim +2
Several recently proposed text-to-speech (TTS) models achieved to generate the speech samples with the human-level quality in the single-speaker and multi-speaker TTS scenarios wit…
Fully Unsupervised Training of Few-shot Keyword Spotting
Dongjune Lee, Minchan Kim, Sung Hwan Mun +2
For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset containing massive target keywords has known to be essential to generalize to arbitrary target keyw…
Disentangled Speaker Representation Learning via Mutual Information Minimization
Sung Hwan Mun, Min Hyun Han, Minchan Kim +2
Domain mismatch problem caused by speaker-unrelated feature has been a major topic in speaker recognition. In this paper, we propose an explicit disentanglement framework to unrave…
HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognition
Ji Won Yoon, Beom Jun Woo, Nam Soo Kim
Pre-training with self-supervised models, such as Hidden-unit BERT (HuBERT) and wav2vec 2.0, has brought significant improvements in automatic speech recognition (ASR). However, th…
Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification
Sung Hwan Mun, Jee-weon Jung, Min Hyun Han +1
The majority of recent state-of-the-art speaker verification architectures adopt multi-scale processing and frequency-channel attention mechanisms. Convolutional layers of these mo…
Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus
Minchan Kim, Myeonghun Jeong, Byoung Jin Choi +3
Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus, which is troublesome to collect. In this paper, we propose a transfer learning framework fo…