Noisy Speech Recognition
3 papers with code • 2 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Noisy Speech Recognition
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Latest papers with no code
Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments
This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication within real multiparty conversational environments.
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.
Speech Recognition With No Speech Or With Noisy Speech Beyond English
In this paper we demonstrate continuous noisy speech recognition using connectionist temporal classification (CTC) model on limited Chinese vocabulary using electroencephalography (EEG) features with no speech signal as input and we further demonstrate single CTC model based continuous noisy speech recognition on limited joint English and Chinese vocabulary using EEG features with no speech signal as input.
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.
A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition
It means that the proposed method is more robust against noisy data and handle these data effectively.
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline
This paper describes a new baseline system for automatic speech recognition (ASR) in the CHiME-4 challenge to promote the development of noisy ASR in speech processing communities by providing 1) state-of-the-art system with a simplified single system comparable to the complicated top systems in the challenge, 2) publicly available and reproducible recipe through the main repository in the Kaldi speech recognition toolkit.
An online sequence-to-sequence model for noisy speech recognition
This is because the models require that the entirety of the input sequence be available at the beginning of inference, an assumption that is not valid for instantaneous speech recognition.
A comprehensive study of batch construction strategies for recurrent neural networks in MXNet
In this work we compare different batch construction methods for mini-batch training of recurrent neural networks.
Invariant Representations for Noisy Speech Recognition
Ensuring such robustness to variability is a challenge in modern day neural network-based ASR systems, especially when all types of variability are not seen during training.
