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Found 19 papers, 6 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.
Make-A-Voice: Unified Voice Synthesis With Discrete Representation
Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common.
C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification
On the basis of the pretrained CSSL model, we further propose to employ a negative sample free SSL objective (i. e., DINO) to fine-tune the speaker embedding network.
UTTS: Unsupervised TTS with Conditional Disentangled Sequential Variational Auto-encoder
We leverage recent advancements in self-supervised speech representation learning as well as speech synthesis front-end techniques for system development.
LAE: Language-Aware Encoder for Monolingual and Multilingual ASR
Experiments conducted on Mandarin-English code-switched speech suggest that the proposed LAE is capable of discriminating different languages in frame-level and shows superior performance on both monolingual and multilingual ASR tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement
Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech communication as well as the front-end speech enhancement for recognition in the conditions when the loudspeaker plays back.
Towards Improved Zero-shot Voice Conversion with Conditional DSVAE
In our experiment on the VCTK dataset, we demonstrate that content embeddings derived from the conditional DSVAE overcome the randomness and achieve a much better phoneme classification accuracy, a stabilized vocalization and a better zero-shot VC performance compared with the competitive DSVAE baseline.
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers
In this paper, we present a novel framework that jointly performs three tasks: speaker diarization, speech separation, and speaker counting.
Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion
A zero-shot voice conversion is performed by feeding an arbitrary speaker embedding and content embeddings to the VAE decoder.
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type.
MetricNet: Towards Improved Modeling For Non-Intrusive Speech Quality Assessment
The objective speech quality assessment is usually conducted by comparing received speech signal with its clean reference, while human beings are capable of evaluating the speech quality without any reference, such as in the mean opinion score (MOS) tests.
Towards Robust Speaker Verification with Target Speaker Enhancement
This paper proposes the target speaker enhancement based speaker verification network (TASE-SVNet), an all neural model that couples target speaker enhancement and speaker embedding extraction for robust speaker verification (SV).
Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning
First, we examine a simple contrastive learning approach (SimCLR) with a momentum contrastive (MoCo) learning framework, where the MoCo speaker embedding system utilizes a queue to maintain a large set of negative examples.
Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training
In order to make timbre conversion more stable and controllable, speaker embedding is further decomposed to the weighted sum of a group of trainable vectors representing different timbre clusters.
Improving RNN Transducer With Target Speaker Extraction and Neural Uncertainty Estimation
Target-speaker speech recognition aims to recognize target-speaker speech from noisy environments with background noise and interfering speakers.
Speech Enhancement
Speech Extraction
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Sound
Audio and Speech Processing
Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition
In this work, we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation
This document briefly describes the systems submitted by the Center for Robust Speech Systems (CRSS) from The University of Texas at Dallas (UTD) to the 2016 National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation (SRE).
