28 citations · 51 across the 9 of their papers we have counts for
11 papers
E-Branchformer: Branchformer with Enhanced merging for speech recognition
Kwangyoun Kim, Felix Wu, Yifan Peng +4
Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the sta…
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
Felix Wu, Kwangyoun Kim, Shinji Watanabe +4
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete repres…
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition
Jing Pan, Tao Lei, Kwangyoun Kim +2
The Transformer architecture has been well adopted as a dominant architecture in most sequence transduction tasks including automatic speech recognition (ASR), since its attention…
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
Felix Wu, Kwangyoun Kim, Jing Pan +3
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture…
Multi-mode Transformer Transducer with Stochastic Future Context
Kwangyoun Kim, Felix Wu, Prashant Sridhar +2
Automatic speech recognition (ASR) models make fewer errors when more surrounding speech information is presented as context. Unfortunately, acquiring a larger future context leads…
Leveraging Pre-trained Language Model for Speech Sentiment Analysis
Suwon Shon, Pablo Brusco, Jing Pan +2
In this paper, we explore the use of pre-trained language models to learn sentiment information of written texts for speech sentiment analysis. First, we investigate how useful a p…