2.4k citations · 5k across the 21 of their papers we have counts for
12 papers · 1 filter
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…
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks
Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin +7
Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community, but have not received as much attention as lower-level tasks like spe…
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
Felix Wu, Kwangyoun Kim, Shinji Watanabe +4
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete repres…
On the Use of External Data for Spoken Named Entity Recognition
Ankita Pasad, Felix Wu, Suwon Shon +2
Spoken language understanding (SLU) tasks involve mapping from speech audio signals to semantic labels. Given the complexity of such tasks, good performance might be expected to re…
SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech
Suwon Shon, Ankita Pasad, Felix Wu +4
Progress in speech processing has been facilitated by shared datasets and benchmarks. Historically these have focused on automatic speech recognition (ASR), speaker identification,…
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
Felix Wu, Kwangyoun Kim, Jing Pan +3
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture…
Revisiting Few-sample BERT Fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar +2
This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cau…
BERTScore: Evaluating Text Generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu +2
We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sente…
FastFusionNet: New State-of-the-Art for DAWNBench SQuAD
Felix Wu, Boyi Li, Lequn Wang +3
In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12]. FusionNet is a high performing reading comprehension architecture, which was designed…
Pay Less Attention with Lightweight and Dynamic Convolutions
Felix Wu, Angela Fan, Alexei Baevski +2
Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current t…
Fast Reading Comprehension with ConvNets
Felix Wu, Ni Lao, John Blitzer +2
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets. Their sequential nature is a natural fit for language, but it also precludes parallelizat…