Search Results for author:
Found 27 papers, 13 papers with code
k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learning
Self-supervised learning (SSL) has achieved great success in speech-related tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
CR-CTC: Consistency regularization on CTC for improved speech recognition
Connectionist Temporal Classification (CTC) is a widely used method for automatic speech recognition (ASR), renowned for its simplicity and computational efficiency.
Ranked #1 on
Speech Recognition
on GigaSpeech TEST
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization
This paper presents a large-scale far-field overlapping speech dataset, crafted to advance research in speech separation, recognition, and speaker diarization.
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation
With the advancement of autonomous driving, ensuring safety during motion planning and navigation is becoming more and more important.
Rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model
Firstly, Gram angular field coding technique is used to encode the time domain signal of the rolling bearing and generate the feature map to retain the complete information of the vibration signal.
Zipformer: A faster and better encoder for automatic speech recognition
The Conformer has become the most popular encoder model for automatic speech recognition (ASR).
Ranked #3 on
Speech Recognition
on WenetSpeech
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context
In this paper, we introduce Libriheavy, a large-scale ASR corpus consisting of 50, 000 hours of read English speech derived from LibriVox.
PromptASR for contextualized ASR with controllable style
An additional style prompt can be given to the text encoder and guide the ASR system to output different styles of transcriptions.
A Surrogate Data Assimilation Model for the Estimation of Dynamical System in a Limited Area
We propose a novel learning-based surrogate data assimilation (DA) model for efficient state estimation in a limited area.
Blank-regularized CTC for Frame Skipping in Neural Transducer
Neural Transducer and connectionist temporal classification (CTC) are popular end-to-end automatic speech recognition systems.
Delay-penalized CTC implemented based on Finite State Transducer
Our work is open-sourced and publicly available https://github. com/k2-fsa/k2.
Feature-Informed Data Assimilation -- Definitions and Illustrative Examples
We introduce a mathematical formulation of feature-informed data assimilation (FIDA).
Fast and parallel decoding for transducer
In this work, we introduce a constrained version of transducer loss to learn strictly monotonic alignments between the sequences; we also improve the standard greedy search and beam search algorithms by limiting the number of symbols that can be emitted per time step in transducer decoding, making it more efficient to decode in parallel with batches.
Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation
Although on-the-fly teacher label generation tackles this issue, the training speed is significantly slower as the teacher model has to be evaluated every batch.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Delay-penalized transducer for low-latency streaming ASR
In streaming automatic speech recognition (ASR), it is desirable to reduce latency as much as possible while having minimum impact on recognition accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Pruned RNN-T for fast, memory-efficient ASR training
The RNN-Transducer (RNN-T) framework for speech recognition has been growing in popularity, particularly for deployed real-time ASR systems, because it combines high accuracy with naturally streaming recognition.
Neural Network Optimal Feedback Control with Guaranteed Local Stability
The proposed architectures are compared against standard neural network feedback controllers through numerical simulations of two high-dimensional nonlinear optimal control problems: stabilization of an unstable Burgers-type partial differential equation, and altitude and course tracking for an unmanned aerial vehicle.
Towards A Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures
Since Deep Learning (DL) backdoor attacks have been revealed as one of the most insidious adversarial attacks, a number of countermeasures have been developed with certain assumptions defined in their respective threat models.
Machine Learning based Optimal Feedback Control for Microgrid Stabilization
A case study is carried out for a microgrid model based on a modified Kundur two-area system to test the real-time performance of the proposed control scheme.
The Observability in Unobservable Systems
In this paper, we introduce the concept of observability of targeted state variables for systems that may not be fully observable.
Data-Driven Computational Methods for the Domain of Attraction and Zubov's Equation
We prove that a neural network approximation exists for the Lyapunov function of power systems such that the approximation error is a cubic polynomial of the number of generators.
Neural network optimal feedback control with enhanced closed loop stability
In this paper we use numerical simulations to demonstrate that typical test accuracy metrics do not effectively capture the ability of an NN controller to stabilize a system.
On how Cognitive Computing will plan your next Systematic Review
Systematic literature reviews (SLRs) are at the heart of evidence-based research, setting the foundation for future research and practice.
Neural Network Approximations of Compositional Functions With Applications to Dynamical Systems
This capability is critical because it enables the analysis of approximation errors for problems for which analytic solutions are not available, such as differential equations and optimal control.
QRnet: optimal regulator design with LQR-augmented neural networks
In this paper we propose a new computational method for designing optimal regulators for high-dimensional nonlinear systems.
BIG-bench Machine Learning
Physics-informed machine learning
Density Propagation with Characteristics-based Deep Learning
Uncertainty propagation in nonlinear dynamic systems remains an outstanding problem in scientific computing and control.
Adaptive Deep Learning for High-Dimensional Hamilton-Jacobi-Bellman Equations
In this paper, we propose a data-driven method to approximate semi-global solutions to HJB equations for general high-dimensional nonlinear systems and compute candidate optimal feedback controls in real-time.
