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20162024
most citedWavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

1.9k citations · 2.8k across the 43 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.SD2023★ 7 cited

WavMark: Watermarking for Audio Generation

Guangyu Chen, Yu Wu, Shujie Liu +3

Recent breakthroughs in zero-shot voice synthesis have enabled imitating a speaker's voice using just a few seconds of recording while maintaining a high level of realism. Alongsid…

eess.AS2023★ 3 cited

On decoder-only architecture for speech-to-text and large language model integration

Jian Wu, Yashesh Gaur, Zhuo Chen +8

Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. Howeve…

cs.CL2023

Accelerating Transducers through Adjacent Token Merging

Yuang Li, Yu Wu, Jinyu Li +1

Recent end-to-end automatic speech recognition (ASR) systems often utilize a Transformer-based acoustic encoder that generates embedding at a high frame rate. However, this design…

cs.CL2023★ 2 cited

Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition

Yuang Li, Yu Wu, Jinyu Li +1

The integration of Language Models (LMs) has proven to be an effective way to address domain shifts in speech recognition. However, these approaches usually require a significant a…

cs.CL2023

Accurate and Structured Pruning for Efficient Automatic Speech Recognition

Huiqiang Jiang, Li Lyna Zhang, Yuang Li +7

Automatic Speech Recognition (ASR) has seen remarkable advancements with deep neural networks, such as Transformer and Conformer. However, these models typically have large model s…

cs.CL2023★ 17 cited

VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation

Tianrui Wang, Long Zhou, Ziqiang Zhang +6

Recent research shows a big convergence in model architecture, training objectives, and inference methods across various tasks for different modalities. In this paper, we propose V…