11 citations · 27 across the 11 of their papers we have counts for
12 papers
Evaluating the Expressive Appropriateness of Speech in Rich Contexts
Tianrui Wang, Ziyang Ma, Yizhou Peng +26
Evaluating expressive speech remains challenging, as existing methods mainly assess emotional intensity and overlook whether a speech sample is expressively appropriate for its con…
Chronological Thinking in Full-Duplex Spoken Dialogue Language Models
Donghang Wu, Haoyang Zhang, Chen Chen +8
Recent advances in spoken dialogue language models (SDLMs) reflect growing interest in shifting from turn-based to full-duplex systems, where the models continuously perceive user…
SEA-Spoof: Bridging The Gap in Multilingual Audio Deepfake Detection for South-East Asian
Jinyang Wu, Nana Hou, Zihan Pan +3
The rapid growth of the digital economy in South-East Asia (SEA) has amplified the risks of audio deepfakes, yet current datasets cover SEA languages only sparsely, leaving models…
Aligning Generative Speech Enhancement with Perceptual Feedback
Haoyang Li, Nana Hou, Yuchen Hu +6
Language Model (LM)-based speech enhancement (SE) has recently emerged as a promising direction, but existing approaches predominantly rely on token-level likelihood objectives tha…
Self-critical Sequence Training for Automatic Speech Recognition
Chen Chen, Yuchen Hu, Nana Hou +3
Although automatic speech recognition (ASR) task has gained remarkable success by sequence-to-sequence models, there are two main mismatches between its training and testing that m…
Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning
Chen Chen, Nana Hou, Yuchen Hu +3
Automated Audio captioning (AAC) is a cross-modal task that generates natural language to describe the content of input audio. Most prior works usually extract single-modality acou…
Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data
Chen Chen, Nana Hou, Yuchen Hu +2
Noise-robust speech recognition systems require large amounts of training data including noisy speech data and corresponding transcripts to achieve state-of-the-art performances in…
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting
Yang Xiao, Nana Hou, Eng Siong Chng
Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. This problem will be more challenging if KWS models are further required…
Dual-Path Style Learning for End-to-End Noise-Robust Speech Recognition
Yuchen Hu, Nana Hou, Chen Chen +1
Automatic speech recognition (ASR) systems degrade significantly under noisy conditions. Recently, speech enhancement (SE) is introduced as front-end to reduce noise for ASR, but i…
Progressive Continual Learning for Spoken Keyword Spotting
Yizheng Huang, Nana Hou, Nancy F. Chen
Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. To tackle such challenges, we propose a progressive continual learning s…
Interactive Feature Fusion for End-to-End Noise-Robust Speech Recognition
Yuchen Hu, Nana Hou, Chen Chen +1
Speech enhancement (SE) aims to suppress the additive noise from a noisy speech signal to improve the speech's perceptual quality and intelligibility. However, the over-suppression…
Multitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech
Duo Ma, Nana Hou, Van Tung Pham +2
To realize robust end-to-end Automatic Speech Recognition(E2E ASR) under radio communication condition, we propose a multitask-based method to joint train a Speech Enhancement (SE)…