Search Results for author:
Found 28 papers, 22 papers with code
Sample-Efficient Diffusion for Text-To-Speech Synthesis
This work introduces Sample-Efficient Speech Diffusion (SESD), an algorithm for effective speech synthesis in modest data regimes through latent diffusion.
Improving ASR Contextual Biasing with Guided Attention
Compared to studies with similar motivations, the proposed loss operates directly on the cross attention weights and is easier to implement.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
On the Effectiveness of Offline RL for Dialogue Response Generation
A common training technique for language models is teacher forcing (TF).
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks
Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spoken language understanding (SLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understanding
Self-supervised speech representation learning (SSL) has shown to be effective in various downstream tasks, but SSL models are usually large and slow.
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks
In this work, we introduce several new annotated SLU benchmark tasks based on freely available speech data, which complement existing benchmarks and address gaps in the SLU evaluation landscape.
Context-aware Fine-tuning of Self-supervised Speech Models
During the fine-tuning stage, we introduce an auxiliary loss that encourages this context embedding vector to be similar to context vectors of surrounding segments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
E-Branchformer: Branchformer with Enhanced merging for speech recognition
Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the state-of-the-art for automatic speech recognition (ASR).
Ranked #11 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data.
Ranked #3 on
Named Entity Recognition (NER)
on SLUE
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
On the Use of External Data for Spoken Named Entity Recognition
In this work we focus on low-resource spoken named entity recognition (NER) and address the question: Beyond self-supervised pre-training, how can we use external speech and/or text data that are not annotated for the task?
SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech
Historically these have focused on automatic speech recognition (ASR), speaker identification, or other lower-level tasks.
Ranked #1 on
Named Entity Recognition (NER)
on SLUE
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-mode Transformer Transducer with Stochastic Future Context
A Multi-mode ASR model can fulfill various latency requirements during inference -- when a larger latency becomes acceptable, the model can process longer future context to achieve higher accuracy and when a latency budget is not flexible, the model can be less dependent on future context but still achieve reliable accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Making Paper Reviewing Robust to Bid Manipulation Attacks
We develop a novel approach for paper bidding and assignment that is much more robust against such attacks.
Attention-based Quantum Tomography
Here we propose the "Attention-based Quantum Tomography" (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state.
Revisiting Few-sample BERT Fine-tuning
We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process.
On Feature Normalization and Data Augmentation
The moments (a. k. a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time.
Ranked #32 on
Domain Generalization
on ImageNet-A
Integrated Triaging for Fast Reading Comprehension
This paper introduces Integrated Triaging, a framework that prunes almost all context in early layers of a network, leaving the remaining (deep) layers to scan only a tiny fraction of the full corpus.
Positional Normalization
A popular method to reduce the training time of deep neural networks is to normalize activations at each layer.
BERTScore: Evaluating Text Generation with BERT
We propose BERTScore, an automatic evaluation metric for text generation.
FastFusionNet: New State-of-the-Art for DAWNBench SQuAD
In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12].
Simplifying Graph Convolutional Networks
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
Ranked #3 on
Text Classification
on Ohsumed
Pay Less Attention with Lightweight and Dynamic Convolutions
We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements.
Ranked #1 on
Machine Translation
on WMT 2017 English-Chinese
Attack Graph Convolutional Networks by Adding Fake Nodes
In this paper, we propose a new type of "fake node attacks" to attack GCNs by adding malicious fake nodes.
An empirical study on evaluation metrics of generative adversarial networks
Evaluating generative adversarial networks (GANs) is inherently challenging.
Fast Reading Comprehension with ConvNets
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets.
On Fairness and Calibration
The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models.
Multi-Scale Dense Networks for Resource Efficient Image Classification
In this paper we investigate image classification with computational resource limits at test time.
General Classification
Handwritten Mathmatical Expression Recognition
+1
