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Found 21 papers, 4 papers with code
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding
To showcase such integration, we performed experiments on carefully designed synthetic datasets for noisy-reverberant multi-channel ST and SLU tasks, which can be used as benchmark corpora for future research.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Conditional Diffusion Probabilistic Model for Speech Enhancement
Speech enhancement is a critical component of many user-oriented audio applications, yet current systems still suffer from distorted and unnatural outputs.
The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research.
Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation
Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry.
End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction
In addition, we train through unfolded iterations of a phase reconstruction algorithm, represented as a series of STFT and inverse STFT layers.
Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective
This study investigates phase reconstruction for deep learning based monaural talker-independent speaker separation in the short-time Fourier transform (STFT) domain.
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement
This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation.
Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge
This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of speech enhancement with 3D Ambisonic microphones.
Locate This, Not That: Class-Conditioned Sound Event DOA Estimation
Existing systems for sound event localization and detection (SELD) typically operate by estimating a source location for all classes at every time instant.
Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks
In this paper, we focus on the cocktail fork problem, which takes a three-pronged approach to source separation by separating an audio mixture such as a movie soundtrack or podcast into the three broad categories of speech, music, and sound effects (SFX - understood to include ambient noise and natural sound events).
Multi-Channel Target Speaker Extraction with Refinement: The WavLab Submission to the Second Clarity Enhancement Challenge
To address the challenges encountered in the CEC2 setting, we introduce four major novelties: (1) we extend the state-of-the-art TF-GridNet model, originally designed for monaural speaker separation, for multi-channel, causal speech enhancement, and large improvements are observed by replacing the TCNDenseNet used in iNeuBe with this new architecture; (2) we leverage a recent dual window size approach with future-frame prediction to ensure that iNueBe-X satisfies the 5 ms constraint on algorithmic latency required by CEC2; (3) we introduce a novel speaker-conditioning branch for TF-GridNet to achieve target speaker extraction; (4) we propose a fine-tuning step, where we compute an additional loss with respect to the target speaker signal compensated with the listener audiogram.
Neural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated Full- and Sub-Band Modeling
We propose FSB-LSTM, a novel long short-term memory (LSTM) based architecture that integrates full- and sub-band (FSB) modeling, for single- and multi-channel speech enhancement in the short-time Fourier transform (STFT) domain.
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios
The CHiME challenges have played a significant role in the development and evaluation of robust automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation
In detail, we explore multi-channel separation methods, mask-based beamforming and complex spectral mapping, as well as the best features to use in the ASR back-end model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction
This pioneering effort aims to set the first benchmark for the AVTSE task, offering fresh insights into enhancing the ac-curacy of back-end speech recognition systems through AVTSE in challenging and real acoustic environments.
Toward Universal Speech Enhancement for Diverse Input Conditions
Currently, there is no universal SE approach that can effectively handle diverse input conditions with a single model.
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction
We propose a multi-task universal speech enhancement (MUSE) model that can perform five speech enhancement (SE) tasks: dereverberation, denoising, speech separation (SS), target speaker extraction (TSE), and speaker counting.
Boosting Unknown-number Speaker Separation with Transformer Decoder-based Attractor
We propose a novel speech separation model designed to separate mixtures with an unknown number of speakers.
Ranked #1 on
Speech Separation
on WSJ0-5mix
USDnet: Unsupervised Speech Dereverberation via Neural Forward Filtering
We show that this novel methodology can promote unsupervised dereverberation of single-source reverberant speech.
Mixture to Mixture: Leveraging Close-talk Mixtures as Weak-supervision for Speech Separation
We propose mixture to mixture (M2M) training, a weakly-supervised neural speech separation algorithm that leverages close-talk mixtures as a weak supervision for training discriminative models to separate far-field mixtures.
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.
