Mixed Speech

Mixed speech, encompassing scenarios with multiple speakers, noise, and overlapping audio, presents a significant challenge in areas like keyword spotting and speech transcription. Current research focuses on developing robust models, often employing transformer architectures and techniques like Mix Training, to effectively extract target information from complex audio signals. These advancements are crucial for improving the accuracy and reliability of speech processing applications in noisy real-world environments, impacting fields ranging from assistive technologies to medical diagnostics. Furthermore, research explores the integration of visual and textual information to enhance the understanding and processing of mixed speech data.

Papers

April 16, 2023

Non-exemplar Class-incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features
Ke Song, Quan Xia, Guoqiang Liang, Zhaojie Chen, Yanning Zhang
Data Augmentation Class Incremental Learning Mixed Speech Exemplar Based Random Augmentation Random Data Augmentation

April 9, 2023

RGB-T Tracking Based on Mixed Attention
Yang Luo, Xiqing Guo, Mingtao Dong, Jin Yu
Multimodal Attention Single Object Tracking Mixed Speech RGB T Tracking

February 17, 2023

Entry Separation using a Mixed Visual and Textual Language Model: Application to 19th century French Trade Directories
Bertrand Duménieu, Edwin Carlinet, Nathalie Abadie, Joseph Chazalon
Application Proficiency Entity Recognition Text Modality Structured Data Separation Performance Natural Language Model Mixed Speech Text Region

February 9, 2023

Mixed-order self-paced curriculum learning for universal lesion detection
Han Li, Hu Han, S. Kevin Zhou
Proximal Curriculum Mixed Speech Universal Lesion Detection Hard Sample Generation

January 2, 2023

Mixed moving average field guided learning for spatio-temporal data
Imma Valentina Curato, Orkun Furat, Lorenzo Proietti, Bennet Stroeh
LeArning Abstract Spatio Temporal Ensemble Forecast Mixed Speech Causal Prediction Ornstein Uhlenbeck Process

November 9, 2022

MACSum: Controllable Summarization with Mixed Attributes
Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang
Summarization Task Human Written Summary Mixed Speech Controllable Summarization Summarization Dataset

June 15, 2022

A Simple Data Mixing Prior for Improving Self-Supervised Learning
Sucheng Ren, Huiyu Wang, Zhengqi Gao, Shengfeng He, Alan Yuille, Yuyin Zhou, Cihang Xie
Self Supervised Learning Self Supervised Self Supervised Representation Learning Recognition Model Mixed Speech Self Supervised Framework Data Mixing

June 2, 2022

Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Ashwinkumar Badanidiyuru, Zhe Feng, Tianxi Li, Haifeng Xu
Reinforcement Learning Episodic Markov Decision Process Automated Conversion Novel Reinforcement Learning Mixed Speech Incremental Model Reward Feedback Reward Delay

May 26, 2022

MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers
Jihao Liu, Xin Huang, Jinliang Zheng, Yu Liu, Hongsheng Li
Pre Training Image Modeling Hierarchical Transformer Masked AutoEncoder Hierarchical Vision Transformer Mixed Speech

January 19, 2022

Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism
Albert Benveniste, Jean-Baptiste Raclet
Probabilistic Graphical Model Probabilistic Programming Mixed Speech Finite Automaton Probabilistic Deterministic Finite Automaton Hybrid Automaton

Mixed Speech

Papers

Non-exemplar Class-incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features

RGB-T Tracking Based on Mixed Attention

Entry Separation using a Mixed Visual and Textual Language Model: Application to 19th century French Trade Directories

Mixed-order self-paced curriculum learning for universal lesion detection

Mixed moving average field guided learning for spatio-temporal data

MACSum: Controllable Summarization with Mixed Attributes

A Simple Data Mixing Prior for Improving Self-Supervised Learning

Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards

MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers

Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism