50 citations · 58 across the 10 of their papers we have counts for
10 papers
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…
Context-aware Fine-tuning of Self-supervised Speech Models
Suwon Shon, Felix Wu, Kwangyoun Kim +3
Self-supervised pre-trained transformers have improved the state of the art on a variety of speech tasks. Due to the quadratic time and space complexity of self-attention, they usu…
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…
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…
Tuplemax Loss for Language Identification
Li Wan, Prashant Sridhar, Yang Yu +2
In many scenarios of a language identification task, the user will specify a small set of languages which he/she can speak instead of a large set of all possible languages. We want…
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
Quan Wang, Hannah Muckenhirn, Kevin Wilson +7
In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We ac…