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Found 23 papers, 16 papers with code
On the Model-Agnostic Multi-Source-Free Unsupervised Domain Adaptation
Specifically, we first conduct source model selection based on the proposed selection principles.
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators
Leveraging the rich linguistic knowledge and strong reasoning abilities of LLMs, our new paradigm can integrate the rich information in N-best candidates to generate a higher-quality translation result.
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition
Recent studies have successfully shown that large language models (LLMs) can be successfully used for generative error correction (GER) on top of the automatic speech recognition (ASR) output.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+3
Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
To this end, we propose to extract a language-space noise embedding from the N-best list to represent the noise conditions of source speech, which can promote the denoising process in GER.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Leveraging A Medical Knowledge Graph into Large Language Models for Diagnosis Prediction
In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr. Knows, inspired by the clinical diagnostic reasoning process.
Noise-aware Speech Enhancement using Diffusion Probabilistic Model
Specifically, we design a noise classification (NC) model to produce acoustic embedding as a noise conditioner for guiding the reverse denoising process.
MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition
In this paper, we aim to learn the shared representations across modalities to bridge their gap.
Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition
In this work, we investigate the noise-invariant visual modality to strengthen robustness of AVSR, which can adapt to any testing noises while without dependence on noisy training data, a. k. a., unsupervised noise adaptation.
Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition
However, most existing AVSR approaches simply fuse the audio and visual features by concatenation, without explicit interactions to capture the deep correlations between them, which results in sub-optimal multimodal representations for downstream speech recognition task.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+3
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition
In this paper, we propose a simple yet effective approach called gradient remedy (GR) to solve interference between task gradients in noise-robust speech recognition, from perspectives of both angle and magnitude.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI
The final trained model was also evaluated on an independent test set by the CMRxMotion organisers, which achieved the classification accuracy of 72. 5% and Cohen's Kappa of 0. 6309 (ranked top 1 in this grand challenge).
On the Latent Holes of VAEs for Text Generation
In this paper, we provide the first focused study on the discontinuities (aka.
On the Latent Holes 🧀 of VAEs for Text Generation
In this paper, we provide the first focused study on the discontinuities (aka.
Affective Decoding for Empathetic Response Generation
Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems.
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI
The proposed method was evaluated on a public brain MRI data set for age estimation.
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation
The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences.
DGST: a Dual-Generator Network for Text Style Transfer
We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer.
FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation.
A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN).
A Stable Variational Autoencoder for Text Modelling
Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data.
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods.
Ranked #7 on
Image Generation
on CelebA 256x256
(FID metric)
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition.
Ranked #4 on
Dialogue Act Classification
on Switchboard corpus
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet
This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes.
