21 citations · 23 across the 6 of their papers we have counts for
6 papers
Peeking Into The Future For Contextual Biasing
Ramaneswaran Selvakumar, Cindy Tseng, Eesung Kim +2
While end-to-end (E2E) automatic speech recognition (ASR) models excel at general transcription, they struggle to recognize rare or unseen named entities (e.g., contact names, loca…
Enhanced Hybrid Transducer and Attention Encoder Decoder with Text Data
Yun Tang, Eesung Kim, Vijendra Raj Apsingekar
A joint speech and text optimization method is proposed for hybrid transducer and attention-based encoder decoder (TAED) modeling to leverage large amounts of text corpus and enhan…
Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning
Eesung Kim, Jae-Jin Jeon, Hyeji Seo +1
Self-supervised learning (SSL) approaches such as wav2vec 2.0 and HuBERT models have shown promising results in various downstream tasks in the speech community. In particular, spe…
JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech
Dan Lim, Sunghee Jung, Eesung Kim
In neural text-to-speech (TTS), two-stage system or a cascade of separately learned models have shown synthesis quality close to human speech. For example, FastSpeech2 transforms a…
Multitask Learning and Joint Optimization for Transformer-RNN-Transducer Speech Recognition
Jae-Jin Jeon, Eesung Kim
Recently, several types of end-to-end speech recognition methods named transformer-transducer were introduced. According to those kinds of methods, transcription networks are gener…
Accelerating RNN Transducer Inference via One-Step Constrained Beam Search
Juntae Kim, Yoonhan Lee
We propose a one-step constrained (OSC) beam search to accelerate recurrent neural network (RNN) transducer (RNN-T) inference. The original RNN-T beam search has a while-loop leadi…