6 citations · 15 across the 12 of their papers we have counts for
12 papers
Why Attend to Everything? Focus is the Key
Hengshuai Yao, Xing Chen, Ahmed Murtadha +8
Standard attention scales quadratically with sequence length. Efficient attention methods reduce this O(n^2) cost, but when retrofitted into pretrained models, they often degrade p…
OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer
Yifan Peng, Jinchuan Tian, William Chen +9
Recent studies have highlighted the importance of fully open foundation models. The Open Whisper-style Speech Model (OWSM) is an initial step towards reproducing OpenAI Whisper usi…
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Yifan Peng, Jinchuan Tian, Brian Yan +13
Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech dat…
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning
William Chen, Jiatong Shi, Brian Yan +6
Multilingual self-supervised learning (SSL) has often lagged behind state-of-the-art (SOTA) methods due to the expenses and complexity required to handle many languages. This furth…
Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute
William Chen, Xuankai Chang, Yifan Peng +3
Self-supervised learning (SSL) has led to great strides in speech processing. However, the resources needed to train these models has become prohibitively large as they continue to…
A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning
Jiyang Tang, William Chen, Xuankai Chang +2
Aphasia is a language disorder that affects the speaking ability of millions of patients. This paper presents a new benchmark for Aphasia speech recognition and detection tasks usi…