9 citations · 30 across the 18 of their papers we have counts for
7 papers · 1 filter
DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding
Suwon Shon, Kwangyoun Kim, Yi-Te Hsu +3
The integration of pre-trained text-based large language models (LLM) with speech input has enabled instruction-following capabilities for diverse speech tasks. This integration re…
Improving ASR Contextual Biasing with Guided Attention
Jiyang Tang, Kwangyoun Kim, Suwon Shon +3
In this paper, we propose a Guided Attention (GA) auxiliary training loss, which improves the effectiveness and robustness of automatic speech recognition (ASR) contextual biasing…
Generative Context-aware Fine-tuning of Self-supervised Speech Models
Suwon Shon, Kwangyoun Kim, Prashant Sridhar +3
When performing tasks like automatic speech recognition or spoken language understanding for a given utterance, access to preceding text or audio provides contextual information ca…
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks
Yifan Peng, Kwangyoun Kim, Felix Wu +7
Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, includi…
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understanding
Yifan Peng, Kwangyoun Kim, Felix Wu +2
Self-supervised speech representation learning (SSL) has shown to be effective in various downstream tasks, but SSL models are usually large and slow. Model compression techniques…
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…
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…