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Found 16 papers, 4 papers with code
基于改进Conformer的新闻领域端到端语音识别(End-to-End Speech Recognition in News Field based on Conformer)
“目前, 开源的中文语音识别数据集多为面向通用领域, 缺少面向新闻领域的开源语音识别语料库, 因此本文构建了面向新闻领域的中文语音识别数据集CHNEWSASR并使用ESPNET-0. 9. 6框架的RNN、Transformer和Conformer等模型对数据集的有效性进行了验证, 实验表明本文所构建的语料在最好的模型上CER为4. 8%, SER为39. 4%。由于新闻联播主持人说话语速相对较快, 本文构建的数据集文本平均长度为28个字符是Aishell1数据集文本平均长度的2倍, 且以往的研究中训练目标函数通常为基于字或词水平, 缺乏明确的句子水平关系, 因此本文提出了一个句子层级的一致性模块与Conformer模型结合直接减少源语音和目标文本的表示差异, 在开源的Aishell1数据集上其CER降低0. 4%, SER降低2%;在CHNEWSASR数据集上其CER降低0. 9%, SER降低3%, 实验结果表明该方法不提升模型参数量的前提下能有效提升语音识别的质量。”
AMNet: An Acoustic Model Network for Enhanced Mandarin Speech Synthesis
This paper presents AMNet, an Acoustic Model Network designed to improve the performance of Mandarin speech synthesis by incorporating phrase structure annotation and local convolution modules.
Leveraging Label Potential for Enhanced Multimodal Emotion Recognition
We introduce a novel model called Label Signal-Guided Multimodal Emotion Recognition (LSGMER) to overcome this limitation.
Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch Boosting
Speech self-supervised learning (SSL) has made great progress in various speech processing tasks, but there is still room for improvement in speech enhancement (SE).
Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) integrates diverse modalities(text, audio, and video) to comprehensively analyze and understand individuals' emotional states.
One Small and One Large for Document-level Event Argument Extraction
The first method introduces the Co and Structure Event Argument Extraction model (CsEAE) based on Small Language Models (SLMs).
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
By incorporating the RASFormer block, our RAS method achieves superior contextual awareness capabilities, leading to remarkable performance.
Multi-class Anomaly Detection
Unsupervised Anomaly Detection
VNet: A GAN-based Multi-Tier Discriminator Network for Speech Synthesis Vocoders
Since the introduction of Generative Adversarial Networks (GANs) in speech synthesis, remarkable achievements have been attained.
Heterogeneous Space Fusion and Dual-Dimension Attention: A New Paradigm for Speech Enhancement
Self-supervised learning has demonstrated impressive performance in speech tasks, yet there remains ample opportunity for advancement in the realm of speech enhancement research.
BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised Embedding
BSS-CFFMA comprises a multi-scale cross-domain feature fusion (MSCFF) block and a residual hybrid multi-attention (RHMA) block.
PCQ: Emotion Recognition in Speech via Progressive Channel Querying
In human-computer interaction (HCI), Speech Emotion Recognition (SER) is a key technology for understanding human intentions and emotions.
Multi-branch CNN and grouping cascade attention for medical image classification
In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images.
MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention
This paper introduces MFHCA, a novel method for Speech Emotion Recognition using Multi-Spatial Fusion and Hierarchical Cooperative Attention on spectrograms and raw audio.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Pay Self-Attention to Audio-Visual Navigation
Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio (emitted from the target) input.
Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach
In this paper, we propose a joint task type and vehicle speed-aware task offloading and resource allocation strategy to decrease the vehicl's energy cost for executing tasks and increase the revenue of the vehicle for processing tasks within the delay constraint.
