activity
20182022
most citedT-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement

12 citations · 12 across the 2 of their papers we have counts for

collaborators

7 papers

cs.LG2022

Contrastive Siamese Network for Semi-supervised Speech Recognition

Soheil Khorram, Jaeyoung Kim, Anshuman Tripathi +3

This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition. c-siam is the first network that extracts…

eess.AS2021

Reducing Streaming ASR Model Delay with Self Alignment

Jaeyoung Kim, Han Lu, Anshuman Tripathi +2

Reducing prediction delay for streaming end-to-end ASR models with minimal performance regression is a challenging problem. Constrained alignment is a well-known existing approach…

cs.IR2020

Deep learning-based citation recommendation system for patents

Jaewoong Choi, Sion Jang, Jaeyoung Kim +3

In this study, we address the challenges in developing a deep learning-based automatic patent citation recommendation system. Although deep learning-based recommendation systems ha…

cs.SD2020

Transformer Transducer: One Model Unifying Streaming and Non-streaming Speech Recognition

Anshuman Tripathi, Jaeyoung Kim, Qian Zhang +2

In this paper we present a Transformer-Transducer model architecture and a training technique to unify streaming and non-streaming speech recognition models into one model. The mod…

cs.SD2019

End-to-End Multi-Task Denoising for the Joint Optimization of Perceptual Speech Metrics

Jaeyoung Kim, Mostafa El-Khamy, Jungwon Lee

Although supervised learning based on a deep neural network has recently achieved substantial improvement on speech enhancement, the existing schemes have either of two critical is…

eess.AS201912 cited

T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement

Jaeyoung Kim, Mostafa El-Khamy, Jungwon Lee

Transformer neural networks (TNN) demonstrated state-of-art performance on many natural language processing (NLP) tasks, replacing recurrent neural networks (RNNs), such as LSTMs o…