51 citations · 85 across the 6 of their papers we have counts for
6 papers
Towards Maximum Likelihood Training for Transducer-based Streaming Speech Recognition
Hyeonseung Lee, Ji Won Yoon, Sungsoo Kim +1
Transducer neural networks have emerged as the mainstream approach for streaming automatic speech recognition (ASR), offering state-of-the-art performance in balancing accuracy and…
MakeSinger: A Semi-Supervised Training Method for Data-Efficient Singing Voice Synthesis via Classifier-free Diffusion Guidance
Semin Kim, Myeonghun Jeong, Hyeonseung Lee +3
In this paper, we propose MakeSinger, a semi-supervised training method for singing voice synthesis (SVS) via classifier-free diffusion guidance. The challenge in SVS lies in the c…
Continuous Monitoring of Blood Pressure with Evidential Regression
Hyeongju Kim, Woo Hyun Kang, Hyeonseung Lee +1
Photoplethysmogram (PPG) signal-based blood pressure (BP) estimation is a promising candidate for modern BP measurements, as PPG signals can be easily obtained from wearable device…
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition
Ji Won Yoon, Hyeonseung Lee, Hyung Yong Kim +2
In recent years, there has been a great deal of research in developing end-to-end speech recognition models, which enable simplifying the traditional pipeline and achieving promisi…
Gated Recurrent Context: Softmax-free Attention for Online Encoder-Decoder Speech Recognition
Hyeonseung Lee, Woo Hyun Kang, Sung Jun Cheon +2
Recently, attention-based encoder-decoder (AED) models have shown state-of-the-art performance in automatic speech recognition (ASR). As the original AED models with global attenti…
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang +2
Flow-based generative models are composed of invertible transformations between two random variables of the same dimension. Therefore, flow-based models cannot be adequately traine…