Bo Xu

Multi-task learning deep neural networks for speech feature denoising

2015

Traditional automatic speech recognition (ASR) systems usually\nget a sharp performance drop when noise presents in\nspeech. To make a robust ASR, we introduce a new model using\nthe multi-task learning deep neural networks (MTL-DNN)\nto solve the speech …

MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition

2021 · arXiv (Cornell University)

In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model by taking a weighted combination of two different speech …

"Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation

2020 · arXiv (Cornell University)

An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs the text in a target language. Existing methods are limited by the amount of parallel corpus. Can we build a system to …

A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction

2023 · arXiv (Cornell University)

This paper focuses on term-status pair extraction from medical dialogues (MD-TSPE), which is essential in diagnosis dialogue systems and the automatic scribe of electronic medical records (EMRs). In the past few years, works on MD-TSPE …

Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction

2023

Sarcasm, as a form of irony conveying mockery and contempt, has been widespread in social media such as Twitter and Weibo, where the sarcastic text is commonly characterized as an incongruity between the surface positive …

Disentangling ID and Modality Effects for Session-based Recommendation

2024

Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence patterns reflected by item IDs, and fine-grained preferences represented by item modalities (e.g., …

SSResNeXt: A Novel Deep Learning Architecture for Multi-class Breast Cancer Pathological Image Classification

2024 · Journal of Computing and Information Technology

Multi-class classification of breast cancer pathological images remains challenging due to complex image features and limited datasets. This study proposes SSResNeXt, a novel deep learning architecture incorporating a new Small-SE-ResNeXt Block with asymmetric convolutions and …

Signal Modulation Recognition via Bias Adjustment-Based Class Incremental Learning

2024 · IEEE Sensors Journal

Automatic modulation classification (AMC) is crucial for electronic warfare, spectrum monitoring, and cognitive radios. Traditional methods, relying on maximum likelihood estimation and manual feature extraction, face challenges, such as complexity, inaccuracy, and limited adaptability. In …

Context-Aware Online Model Splitting and Device Association for Semi-Decentralized Federated Learning in Internet of Things

2026 · Sensors

As a distributed approach to Artificial Intelligence (AI) model construction over wireless networks, federated learning (FL) based on multi-device collaborative training can protect data privacy, as well as increase the computing load of local model …

Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification

2016

Relation classification is an important semantic processing task in the field of natural language processing (NLP). State-ofthe-art systems still rely on lexical resources such as WordNet or NLP systems like dependency parser and named entity …

Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

2018

Zhen Yang, Wei Chen, Feng Wang, Bo Xu. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

2017 · arXiv (Cornell University)

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. …

VOPRec: Vector Representation Learning of Papers with Text Information and Structural Identity for Recommendation

2018 · IEEE Transactions on Emerging Topics in Computing

Finding relevant papers is a non-trivial problem for scholars due to the tremendous amount of academic information in the era of scholarly big data. Scientific paper recommendation systems have been developed to solve such problem …

Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition

2018

Recurrent sequence-to-sequence models using encoder-decoder architecture have made great progress in speech recognition task. However, they suffer from the drawback of slow training speed because the internal recurrence limits the training parallelization. In this paper, …

Self-attention Aligner: A Latency-control End-to-end Model for ASR Using Self-attention Network and Chunk-hopping

2019

Self-attention network, an attention-based feedforward neural network, has recently shown the potential to replace recurrent neural networks (RNNs) in a variety of NLP tasks. However, it is not clear if the self-attention network could be …