Speech Enhancement
217 papers with code • 12 benchmarks • 19 datasets
Speech Enhancement is a signal processing task that involves improving the quality of speech signals captured under noisy or degraded conditions. The goal of speech enhancement is to make speech signals clearer, more intelligible, and more pleasant to listen to, which can be used for various applications such as voice recognition, teleconferencing, and hearing aids.
( Image credit: A Fully Convolutional Neural Network For Speech Enhancement )
Benchmarks
These leaderboards are used to track progress in Speech Enhancement
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Speech Enhancement models and implementationsDatasets
AVA
WHAM!
CHiME-5
VoiceBank + DEMAND
TIMIT
AVA-ActiveSpeaker
Subtasks
Latest papers with no code
Advanced Artificial Intelligence Algorithms in Cochlear Implants: Review of Healthcare Strategies, Challenges, and Perspectives
Automatic speech recognition (ASR) plays a pivotal role in our daily lives, offering utility not only for interacting with machines but also for facilitating communication for individuals with either partial or profound hearing impairments.
SuperME: Supervised and Mixture-to-Mixture Co-Learning for Speech Enhancement and Robust ASR
When paired close-talk and far-field mixtures are available for training, M2M realizes speech enhancement by training a deep neural network (DNN) to produce speech and noise estimates in a way such that they can be linearly filtered to reconstruct the close-talk and far-field mixtures.
A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement
Self-supervised learned models have been found to be very effective for certain speech tasks such as automatic speech recognition, speaker identification, keyword spotting and others.
Investigation of Adapter for Automatic Speech Recognition in Noisy Environment
Adapting an automatic speech recognition (ASR) system to unseen noise environments is crucial.
Audio-Visual Speech Enhancement in Noisy Environments via Emotion-Based Contextual Cues
By integrating emotional features, the proposed system achieves significant improvements in both objective and subjective assessments of speech quality and intelligibility, especially in challenging noise environments.
SICRN: Advancing Speech Enhancement through State Space Model and Inplace Convolution Techniques
Speech enhancement aims to improve speech quality and intelligibility, especially in noisy environments where background noise degrades speech signals.
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
In this work, we propose Mel-FullSubNet, a single-channel Mel-spectrogram denoising and dereverberation network for improving both speech quality and automatic speech recognition (ASR) performance.
Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network
In this study, we present a novel weighting prediction approach, which explicitly learns the task relationships from downstream training information to address the core challenge of universal speech enhancement.
SECP: A Speech Enhancement-Based Curation Pipeline For Scalable Acquisition Of Clean Speech
In this paper, we address this issue by outlining Speech Enhancement-based Curation Pipeline (SECP) which serves as a framework to onboard clean speech.
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model
Recently, Denoising Diffusion Probabilistic Models (DDPMs) have attained leading performances across a diverse range of generative tasks.
