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Found 22 papers, 6 papers with code
Text-To-Speech Synthesis In The Wild
The recent literature nonetheless shows efforts to train TTS systems using data collected in the wild.
Towards Robust Speech Representation Learning for Thousands of Languages
We propose XEUS, a Cross-lingual Encoder for Universal Speech, trained on over 1 million hours of data across 4057 languages, extending the language coverage of SSL models 4-fold.
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement
The last decade has witnessed significant advancements in deep learning-based speech enhancement (SE).
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech Enhancement
Deep learning-based speech enhancement (SE) models have achieved impressive performance in the past decade.
SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition
Existing speech language models typically utilize task-dependent prompt tokens to unify various speech tasks in a single model.
ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models
First, we provide an open-source platform for researchers in the speaker recognition community to effortlessly build models.
Ranked #1 on
Speaker Recognition
on VoxCeleb1
(using extra training data)
Improving Design of Input Condition Invariant Speech Enhancement
In this paper we propose novel architectures to improve the input condition invariant SE model so that performance in simulated conditions remains competitive while real condition degradation is much mitigated.
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.
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.
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning
We show that further efficiency can be achieved with a vanilla HuBERT Base model, which can maintain 94% of XLS-R's performance with only 3% of the data, 4 GPUs, and limited trials.
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Pre-training speech models on large volumes of data has achieved remarkable success.
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
Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition
Self-supervised learning (SSL) based speech pre-training has attracted much attention for its capability of extracting rich representations learned from massive unlabeled data.
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
End-to-End Multi-speaker ASR with Independent Vector Analysis
We develop an end-to-end system for multi-channel, multi-speaker automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions
The deep learning based time-domain models, e. g. Conv-TasNet, have shown great potential in both single-channel and multi-channel speech enhancement.
Separating Long-Form Speech with Group-Wise Permutation Invariant Training
Multi-talker conversational speech processing has drawn many interests for various applications such as meeting transcription.
End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend
Recently, the end-to-end approach has been successfully applied to multi-speaker speech separation and recognition in both single-channel and multichannel conditions.
End-to-End Multi-speaker Speech Recognition with Transformer
Recently, fully recurrent neural network (RNN) based end-to-end models have been proven to be effective for multi-speaker speech recognition in both the single-channel and multi-channel scenarios.
MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition
In this work, we propose a novel neural sequence-to-sequence (seq2seq) architecture, MIMO-Speech, which extends the original seq2seq to deal with multi-channel input and multi-channel output so that it can fully model multi-channel multi-speaker speech separation and recognition.
A Comparative Study on Transformer vs RNN in Speech Applications
Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS).
Ranked #13 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
