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Found 54 papers, 8 papers with code
When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework
In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN).
Student Classroom Behavior Detection based on YOLOv7-BRA and Multi-Model Fusion
We constructed a dataset, which contained 11, 248 labels and 4, 001 images, with an emphasis on the common behavior of raising hands in a classroom setting (Student Classroom Behavior dataset, SCB-Dataset).
Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma
This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images.
Contextual adversarial attack against aerial detection in the physical world
We propose an innovative contextual attack method against aerial detection in real scenarios, which achieves powerful attack performance and transfers well between various aerial object detectors without smearing or blocking the interested objects to hide.
CBA: Contextual Background Attack against Optical Aerial Detection in the Physical World
To further strengthen the attack performance, the adversarial patches are forced to be outside targets during training, by which the detected objects of interest, both on and outside patches, benefit the accumulation of attack efficacy.
When Quantum Information Technologies Meet Blockchain in Web 3.0
Therefore, in this paper, we introduce a quantum blockchain-driven Web 3. 0 framework that provides information-theoretic security for decentralized data transferring and payment transactions.
Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts
Employing a monaural speech separation (SS) model as a front-end for automatic speech recognition (ASR) involves balancing two kinds of trade-offs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition
In this paper, we investigate SSL for streaming multi-talker speech recognition, which generates transcriptions of overlapping speakers in a streaming fashion.
Speech separation with large-scale self-supervised learning
Compared with a supervised baseline and the WavLM-based SS model using feature embeddings obtained with the previously released 94K hours trained WavLM, our proposed model obtains 15. 9% and 11. 2% of relative word error rate (WER) reductions, respectively, for a simulated far-field speech mixture test set.
Simulating realistic speech overlaps improves multi-talker ASR
Multi-talker automatic speech recognition (ASR) has been studied to generate transcriptions of natural conversation including overlapping speech of multiple speakers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition
To combine the best of both technologies, we newly design a t-SOT-based ASR model that generates a serialized multi-talker transcription based on two separated speech signals from VarArray.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions
The DLMP provides a solution that can be essential for competitive market operation in future distribution systems.
Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separation
In this paper, we propose a three-stage training scheme for the CSS model that can leverage both supervised data and extra large-scale unsupervised real-world conversational data.
Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings
The proposed speaker embedding, named t-vector, is extracted synchronously with the t-SOT ASR model, enabling joint execution of speaker identification (SID) or speaker diarization (SD) with the multi-talker transcription with low latency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Streaming Multi-Talker ASR with Token-Level Serialized Output Training
This paper proposes a token-level serialized output training (t-SOT), a novel framework for streaming multi-talker automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
PickNet: Real-Time Channel Selection for Ad Hoc Microphone Arrays
Since PickNet utilizes only limited acoustic context at each time frame, the system using the proposed model works in real time and is robust to changes in acoustic conditions.
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
The reconstruction module is used for auxiliary learning to improve the noise robustness of the learned representation and thus is not required during inference.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
Personalized Speech Enhancement: New Models and Comprehensive Evaluation
Our results show that the proposed models can yield better speech recognition accuracy, speech intelligibility, and perceptual quality than the baseline models, and the multi-task training can alleviate the TSOS issue in addition to improving the speech recognition accuracy.
All-neural beamformer for continuous speech separation
Recently, the all deep learning MVDR (ADL-MVDR) model was proposed for neural beamforming and demonstrated superior performance in a target speech extraction task using pre-segmented input.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
VarArray: Array-Geometry-Agnostic Continuous Speech Separation
Continuous speech separation using a microphone array was shown to be promising in dealing with the speech overlap problem in natural conversation transcription.
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR
Similar to the target-speaker voice activity detection (TS-VAD)-based diarization method, the E2E SA-ASR model is applied to estimate speech activity of each speaker while it has the advantages of (i) handling unlimited number of speakers, (ii) leveraging linguistic information for speaker diarization, and (iii) simultaneously generating speaker-attributed transcriptions.
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback
A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations.
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio
Our evaluation on the AMI meeting corpus reveals that after fine-tuning with a small real data, the joint system performs 8. 9--29. 9% better in accuracy compared to the best modular system while the modular system performs better before such fine-tuning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Saliency-Guided Image Translation
In this paper, we propose a novel task for saliency-guided image translation, with the goal of image-to-image translation conditioned on the user specified saliency map.
Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement
Performance analysis is also carried out by changing the ASR model, the data used for the ASR-step, and the schedule of the two update steps.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-End Speaker-Attributed ASR with Transformer
This paper presents our recent effort on end-to-end speaker-attributed automatic speech recognition, which jointly performs speaker counting, speech recognition and speaker identification for monaural multi-talker audio.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone
Transcribing meetings containing overlapped speech with only a single distant microphone (SDM) has been one of the most challenging problems for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Continuous Speech Separation with Ad Hoc Microphone Arrays
Prior studies show, with a spatial-temporalinterleaving structure, neural networks can efficiently utilize the multi-channel signals of the ad hoc array.
Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System
In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud systems to improve the long-term throughput rate of request processing.
EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles
After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas.
A Systematic Review of the Efforts and Hindrances of Modeling and Simulation of CAR T-cell Therapy
Chimeric Antigen Receptor (CAR) T-cell therapy is an immunotherapy that has recently become highly instrumental in the fight against life-threatening diseases.
Reinforcement Learning with Latent Flow
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings
Then, we propose a novel method using a sequence-to-sequence model, called hypothesis stitcher.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASR
Recently, an end-to-end speaker-attributed automatic speech recognition (E2E SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker identification for monaural overlapped speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
OCT-GAN: Single Step Shadow and Noise Removal from Optical Coherence Tomography Images of the Human Optic Nerve Head
Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians.
Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker Recordings
However, the model required prior knowledge of speaker profiles to perform speaker identification, which significantly limited the application of the model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers
We propose an end-to-end speaker-attributed automatic speech recognition model that unifies speaker counting, speech recognition, and speaker identification on monaural overlapped speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications.
Serialized Output Training for End-to-End Overlapped Speech Recognition
We also show that the SOT models can accurately count the number of speakers in the input audio.
Global Adaptive Generative Adjustment
Many traditional signal recovery approaches can behave well basing on the penalized likelihood.
A practical two-stage training strategy for multi-stream end-to-end speech recognition
The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images
Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast: a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow) and compared to compensated images.
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 #7 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network.
Multi-Stream End-to-End Speech Recognition
Two representative framework have been proposed and discussed, which are Multi-Encoder Multi-Resolution (MEM-Res) framework and Multi-Encoder Multi-Array (MEM-Array) framework, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions
This report investigates the ability of E2E ASR from standard close-talk to far-field applications by encompassing entire multichannel speech enhancement and ASR components within the S2S model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Exploring Methods for the Automatic Detection of Errors in Manual Transcription
Quality of data plays an important role in most deep learning tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Attention Based Glaucoma Detection: A Large-scale Database and CNN Model
The attention maps of the ophthalmologists are also collected in LAG database through a simulated eye-tracking experiment.
Multi-encoder multi-resolution framework for end-to-end speech recognition
In this work, we present a novel Multi-Encoder Multi-Resolution (MEMR) framework based on the joint CTC/Attention model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Stream attention-based multi-array end-to-end speech recognition
Automatic Speech Recognition (ASR) using multiple microphone arrays has achieved great success in the far-field robustness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
For all the ONH tissues, the mean CNR increased from $3. 50 \pm 0. 56$ (single-frame) to $7. 63 \pm 1. 81$ (denoised).
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication.
ABtree: An Algorithm for Subgroup-Based Treatment Assignment
Given two possible treatments, there may exist subgroups who benefit greater from one treatment than the other.
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge
This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
