Search Results for author:
Found 24 papers, 9 papers with code
Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection
This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition
Experimental results based on the ACII Challenge 2022 dataset demonstrate the superior performance of the proposed system and the effectiveness of considering multiple relationships using hierarchical regression chain models.
Learning Domain Invariant Prompt for Vision-Language Models
Prompt learning is one of the most effective and trending ways to adapt powerful vision-language foundation models like CLIP to downstream datasets by tuning learnable prompt vectors with very few samples.
SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition
In this paper, we propose SoftCorrect with a soft error detection mechanism to avoid the limitations of both explicit and implicit error detection.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Understanding Omission in Dialogue Summarization
By analyzing this dataset, we find that a large improvement in summarization quality can be achieved by providing ground-truth omission labels for the summarization model to recover omission information, which demonstrates the importance of omission detection for omission mitigation in dialogue summarization.
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech
Specifically, we first pre-train an encoder-decoder framework in an automatic speech recognition (ASR) objective by using speech-to-text dataset, and then fine-tune ASR encoder on the cleft palate dataset for hypernasality estimation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One
Autoregressive generative models are commonly used, especially for those tasks involving sequential data.
Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling
Previous works on sentence scoring mainly adopted either causal language modeling (CLM) like GPT or masked language modeling (MLM) like BERT, which have some limitations: 1) CLM only utilizes unidirectional information for the probability estimation of a sentence without considering bidirectional context, which affects the scoring quality; 2) MLM can only estimate the probability of partial tokens at a time and thus requires multiple forward passes to estimate the probability of the whole sentence, which incurs large computation and time cost.
Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech
However, the works apply pre-training with character-based units to enhance the TTS phoneme encoder, which is inconsistent with the TTS fine-tuning that takes phonemes as input.
A study on the efficacy of model pre-training in developing neural text-to-speech system
However, in terms of ultimately achieved system performance for target speaker(s), the actual benefits of model pre-training are uncertain and unstable, depending very much on the quantity and text content of training data.
Analyzing and Mitigating Interference in Neural Architecture Search
In this paper, we investigate the interference issue by sampling different child models and calculating the gradient similarity of shared operators, and observe: 1) the interference on a shared operator between two child models is positively correlated with the number of different operators; 2) the interference is smaller when the inputs and outputs of the shared operator are more similar.
DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling
In this paper, we develop DeepRapper, a Transformer-based rap generation system that can model both rhymes and rhythms.
PVT v2: Improved Baselines with Pyramid Vision Transformer
We hope this work will facilitate state-of-the-art Transformer researches in computer vision.
Ranked #60 on
Object Detection
on COCO minival
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search
The technical challenge of NAS-BERT is that training a big supernet on the pre-training task is extremely costly.
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Unlike the recently-proposed Transformer model (e. g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks.
Ranked #5 on
Semantic Segmentation
on SynPASS
Task-Agnostic and Adaptive-Size BERT Compression
NAS-BERT trains a big supernet on a carefully designed search space containing various architectures and outputs multiple compressed models with adaptive sizes and latency.
SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint
Automatic song writing aims to compose a song (lyric and/or melody) by machine, which is an interesting topic in both academia and industry.
Neural Machine Translation with Error Correction
Neural machine translation (NMT) generates the next target token given as input the previous ground truth target tokens during training while the previous generated target tokens during inference, which causes discrepancy between training and inference as well as error propagation, and affects the translation accuracy.
LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning
While pre-training and fine-tuning, e. g., BERT~\citep{devlin2018bert}, GPT-2~\citep{radford2019language}, have achieved great success in language understanding and generation tasks, the pre-trained models are usually too big for online deployment in terms of both memory cost and inference speed, which hinders them from practical online usage.
MPNet: Masked and Permuted Pre-training for Language Understanding
Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to address this problem.
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Pre-training and fine-tuning, e. g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks.
Generating Adversarial Examples With Conditional Generative Adversarial Net
Recently, deep neural networks have significant progress and successful application in various fields, but they are found vulnerable to attack instances, e. g., adversarial examples.
Hybrid Self-Attention Network for Machine Translation
The encoder-decoder is the typical framework for Neural Machine Translation (NMT), and different structures have been developed for improving the translation performance.
Double Path Networks for Sequence to Sequence Learning
In this work we propose Double Path Networks for Sequence to Sequence learning (DPN-S2S), which leverage the advantages of both models by using double path information fusion.
