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Found 16 papers, 8 papers with code
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models
Speech enhancement aims to improve the quality of speech signals in terms of quality and intelligibility, and speech editing refers to the process of editing the speech according to specific user needs.
Unifying Robustness and Fidelity: A Comprehensive Study of Pretrained Generative Methods for Speech Enhancement in Adverse Conditions
Enhancing speech signal quality in adverse acoustic environments is a persistent challenge in speech processing.
Rethinking Voice-Face Correlation: A Geometry View
Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion.
Sequence-Level Knowledge Distillation for Class-Incremental End-to-End Spoken Language Understanding
The ability to learn new concepts sequentially is a major weakness for modern neural networks, which hinders their use in non-stationary environments.
Backdoor Attacks with Input-unique Triggers in NLP
Backdoor attack aims at inducing neural models to make incorrect predictions for poison data while keeping predictions on the clean dataset unchanged, which creates a considerable threat to current natural language processing (NLP) systems.
PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech Enhancement
We can add this criterion as an auxiliary loss to any model that produces speech, to optimize speech outputs to match the values of clean speech in these features.
TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement
We propose an objective for perceptual quality based on temporal acoustic parameters.
Simulating realistic speech overlaps improves multi-talker ASR
Multi-talker automatic speech recognition (ASR) has been studied to generate transcriptions of natural conversation including overlapping speech of multiple speakers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Online Continual Learning of End-to-End Speech Recognition Models
Continual Learning, also known as Lifelong Learning, aims to continually learn from new data as it becomes available.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving Speech Enhancement through Fine-Grained Speech Characteristics
We first identify key acoustic parameters that have been found to correlate well with voice quality (e. g. jitter, shimmer, and spectral flux) and then propose objective functions which are aimed at reducing the difference between clean speech and enhanced speech with respect to these features.
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis
Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels.
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition
Deep neural networks have been shown as a class of useful tools for addressing signal recognition issues in recent years, especially for identifying the nonlinear feature structures of signals.
Self-supervised Representation Learning with Relative Predictive Coding
This paper introduces Relative Predictive Coding (RPC), a new contrastive representation learning objective that maintains a good balance among training stability, minibatch size sensitivity, and downstream task performance.
Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults.
Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis
The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language.
Complex Transformer: A Framework for Modeling Complex-Valued Sequence
While deep learning has received a surge of interest in a variety of fields in recent years, major deep learning models barely use complex numbers.
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