30 citations · 41 across the 22 of their papers we have counts for
10 papers · 1 filter
Leveraging Language Information for Target Language Extraction
Mehmet Sinan Yıldırım, Ruijie Tao, Wupeng Wang +2
Target Language Extraction aims to extract speech in a specific language from a mixture waveform that contains multiple speakers speaking different languages. The human auditory sy…
Audio Deepfake Verification
Li Wang, Junyi Ao, Linyong Gan +3
With the rapid development of deepfake technology, simply making a binary judgment of true or false on audio is no longer sufficient to meet practical needs. Accurately determining…
Solla: Towards a Speech-Oriented LLM That Hears Acoustic Context
Junyi Ao, Dekun Chen, Xiaohai Tian +6
Large Language Models (LLMs) have recently shown remarkable ability to process not only text but also multimodal inputs such as speech and audio. However, most existing models prim…
SA-WavLM: Speaker-Aware Self-Supervised Pre-training for Mixture Speech
Jingru Lin, Meng Ge, Junyi Ao +2
It was shown that pre-trained models with self-supervised learning (SSL) techniques are effective in various downstream speech tasks. However, most such models are trained on singl…
Text-guided HuBERT: Self-Supervised Speech Pre-training via Generative Adversarial Networks
Duo Ma, Xianghu Yue, Junyi Ao +2
Human language can be expressed in either written or spoken form, i.e. text or speech. Humans can acquire knowledge from text to improve speaking and listening. However, the quest…
The NUS-HLT System for ICASSP2024 ICMC-ASR Grand Challenge
Meng Ge, Yizhou Peng, Yidi Jiang +6
This paper summarizes our team's efforts in both tracks of the ICMC-ASR Challenge for in-car multi-channel automatic speech recognition. Our submitted systems for ICMC-ASR Challeng…
Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder
Jingru Lin, Xianghu Yue, Junyi Ao +1
Acoustic word embeddings (AWEs) aims to map a variable-length speech segment into a fixed-dimensional representation. High-quality AWEs should be invariant to variations, such as d…
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
Rui Wang, Qibing Bai, Junyi Ao +6
Self-supervised speech representation learning has shown promising results in various speech processing tasks. However, the pre-trained models, e.g., HuBERT, are storage-intensive…
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
Junyi Ao, Rui Wang, Long Zhou +11
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores th…
Multi-View Self-Attention Based Transformer for Speaker Recognition
Rui Wang, Junyi Ao, Long Zhou +5
Initially developed for natural language processing (NLP), Transformer model is now widely used for speech processing tasks such as speaker recognition, due to its powerful sequenc…