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Found 27 papers, 13 papers with code
Attention is All You Need in Speech Separation
Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism.
Ranked #7 on
Speech Separation
on WSJ0-3mix
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
Exploring Self-Attention Mechanisms for Speech Separation
In particular, we extend our previous findings on the SepFormer by providing results on more challenging noisy and noisy-reverberant datasets, such as LibriMix, WHAM!, and WHAMR!.
Ranked #1 on
Speech Enhancement
on WHAM!
Resource-Efficient Separation Transformer
Transformers have recently achieved state-of-the-art performance in speech separation.
REAL-M: Towards Speech Separation on Real Mixtures
First, we release the REAL-M dataset, a crowd-sourced corpus of real-life mixtures.
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
TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch
TorchAudio is an open-source audio and speech processing library built for PyTorch.
Filterbank design for end-to-end speech separation
Also, we validate the use of parameterized filterbanks and show that complex-valued representations and masks are beneficial in all conditions.
LibriMix: An Open-Source Dataset for Generalizable Speech Separation
Most deep learning-based speech separation models today are benchmarked on it.
Audio and Speech Processing
The impact of non-target events in synthetic soundscapes for sound event detection
Detection and Classification Acoustic Scene and Events Challenge 2021 Task 4 uses a heterogeneous dataset that includes both recorded and synthetic soundscapes.
Learning to Rank Microphones for Distant Speech Recognition
Fully exploiting ad-hoc microphone networks for distant speech recognition is still an open issue.
Low-Latency Speech Separation Guided Diarization for Telephone Conversations
In particular, we compare two low-latency speech separation models.
A Time-Frequency Generative Adversarial based method for Audio Packet Loss Concealment
Packet loss is a major cause of voice quality degradation in VoIP transmissions with serious impact on intelligibility and user experience.
The Speed Submission to DIHARD II: Contributions & Lessons Learned
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team.
Learning Filterbanks for End-to-End Acoustic Beamforming
We perform a detailed analysis using the recent Clarity Challenge data and show that by using learnt filterbanks it is possible to surpass oracle-mask based beamforming for short windows.
Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection
In many speech-enabled human-machine interaction scenarios, user speech can overlap with the device playback audio.
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.
Conversational Speech Separation: an Evaluation Study for Streaming Applications
Continuous speech separation (CSS) is a recently proposed framework which aims at separating each speaker from an input mixture signal in a streaming fashion.
Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system
The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset.
Graph-based Representation of Audio signals for Sound Event Classification
In recent years there has been a considerable rise in interest towards Graph Representation and Learning techniques, especially in such cases where data has intrinsically a graph- like structure: social networks, molecular lattices, or semantic interactions, just to name a few.
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.
End-to-End Integration of Speech Separation and Voice Activity Detection for Low-Latency Diarization of Telephone Conversations
Finally, we also show that the separated signals can be readily used also for automatic speech recognition, reaching performance close to using oracle sources in some configurations.
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.
An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings
We found that, among all methods considered, EEND-vector clustering (EEND-VC) offers the best trade-off in terms of computing requirements and performance.
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
One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition
This paper presents a novel framework for joint speaker diarization (SD) and automatic speech recognition (ASR), named SLIDAR (sliding-window diarization-augmented recognition).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
