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Found 28 papers, 7 papers with code
A Refining Underlying Information Framework for Monaural Speech Enhancement
By bridging the speech enhancement and the Information Bottleneck principle in this letter, we rethink a universal plug-and-play strategy and propose a Refining Underlying Information framework called RUI to rise to the challenges both in theory and practice.
Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound
In this paper, we study the mistake bound of online kernel learning on a budget.
High-Fidelity Speech Synthesis with Minimal Supervision: All Using Diffusion Models
To address these issues, we propose a minimally-supervised high-fidelity speech synthesis method, where all modules are constructed based on the diffusion models.
Learning Speech Representation From Contrastive Token-Acoustic Pretraining
However, existing contrastive learning methods in the audio field focus on extracting global descriptive information for downstream audio classification tasks, making them unsuitable for TTS, VC, and ASR tasks.
Minimally-Supervised Speech Synthesis with Conditional Diffusion Model and Language Model: A Comparative Study of Semantic Coding
However, existing methods suffer from three problems: the high dimensionality and waveform distortion of discrete speech representations, the prosodic averaging problem caused by the duration prediction model in non-autoregressive frameworks, and the information redundancy and dimension explosion problems of existing semantic encoding methods.
Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation
Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available.
Cross-modal Audio-visual Co-learning for Text-independent Speaker Verification
Visual speech (i. e., lip motion) is highly related to auditory speech due to the co-occurrence and synchronization in speech production.
MIMO-DBnet: Multi-channel Input and Multiple Outputs DOA-aware Beamforming Network for Speech Separation
Recently, many deep learning based beamformers have been proposed for multi-channel speech separation.
Monolingual Recognizers Fusion for Code-switching Speech Recognition
It has two stages: the speech awareness (SA) stage and the language fusion (LF) stage.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Deep Spectro-temporal Artifacts for Detecting Synthesized Speech
The Audio Deep Synthesis Detection (ADD) Challenge has been held to detect generated human-like speech.
VCSE: Time-Domain Visual-Contextual Speaker Extraction Network
In the first stage, we pre-extract a target speech with visual cues and estimate the underlying phonetic sequence.
MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with Unknown Number of Sound Sources
These algorithms are usually achieved by mapping the multi-channel audio input to the single output (i. e. overall spatial pseudo-spectrum (SPS) of all sources), that is called MISO.
Language-specific Characteristic Assistance for Code-switching Speech Recognition
Dual-encoder structure successfully utilizes two language-specific encoders (LSEs) for code-switching speech recognition.
Iterative Sound Source Localization for Unknown Number of Sources
Sound source localization aims to seek the direction of arrival (DOA) of all sound sources from the observed multi-channel audio.
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
However, the existing contrastive learning methods are inadequate for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e. g., meta-path) in graph data while ignore the noises that may exist in both node attributes and graph topologies.
TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding
Therefore, in the most current state-of-the-art network architectures, only a few branches corresponding to a limited number of temporal scales could be designed for speaker embeddings.
L-SpEx: Localized Target Speaker Extraction
Speaker extraction aims to extract the target speaker's voice from a multi-talker speech mixture given an auxiliary reference utterance.
Using multiple reference audios and style embedding constraints for speech synthesis
The end-to-end speech synthesis model can directly take an utterance as reference audio, and generate speech from the text with prosody and speaker characteristics similar to the reference audio.
Exploring Deep Learning for Joint Audio-Visual Lip Biometrics
Audio-visual (AV) lip biometrics is a promising authentication technique that leverages the benefits of both the audio and visual modalities in speech communication.
Multi-stage Speaker Extraction with Utterance and Frame-Level Reference Signals
Speaker extraction requires a sample speech from the target speaker as the reference.
SpEx+: A Complete Time Domain Speaker Extraction Network
To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+.
Ranked #1 on
Speech Extraction
on WSJ0-2mix-extr
Speech Extraction
Audio and Speech Processing
Sound
Constructing Accurate and Efficient Deep Spiking Neural Networks with Double-threshold and Augmented Schemes
We examine the performance of our methods based on MNIST, Fashion-MNIST and CIFAR10 datasets.
Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning
They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing.
Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection
Most existing AU detection works considering AU relationships are relying on probabilistic graphical models with manually extracted features.
Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning
Our framework is a unifying system with a consistent integration of three major functional parts which are sparse encoding, efficient learning and robust readout.
Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning
In this paper, we propose a novel neural Tensor network framework with Interactive Attention and Sparse Learning (TIASL) for implicit discourse relation recognition.
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention
They ignore that one discusses diverse topics when dynamically interacting with different people.
Speech Emotion Recognition Considering Local Dynamic Features
Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences.
