Robust Speech Recognition

14 papers with code • 0 benchmarks • 1 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

Very Deep Convolutional Neural Networks for Robust Speech Recognition

Anustup900/Tensorflow-Speech-Recognition 2 Oct 2016

On the Aurora 4 task, the very deep CNN achieves a WER of 8. 81%, further 7. 99% with auxiliary feature joint training, and 7. 09% with LSTM-RNN joint decoding.

Scalable Factorized Hierarchical Variational Autoencoder Training

wnhsu/ScalableFHVAE 9 Apr 2018

Deep generative models have achieved great success in unsupervised learning with the ability to capture complex nonlinear relationships between latent generating factors and observations.

Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition

wangkenpu/rsrgan 27 Mar 2018

First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset.

Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition

vivivic/speech-domain-adaptation-DRL 12 Apr 2019

The latent variables allow us to convert the domain of speech according to its context and domain representation.

Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural Networks

doglic/asr 23 Jun 2019

We investigate the potential of stochastic neural networks for learning effective waveform-based acoustic models.

Multi-task self-supervised learning for Robust Speech Recognition

santi-pdp/pase 25 Jan 2020

We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks.

Domain Adaptation Using Class Similarity for Robust Speech Recognition

zhu-han/ASR-Adaption-Class-Similarity 5 Nov 2020

Then, for each class, probabilities of this class are used to compute a mean vector, which we refer to as mean soft labels.

An Investigation of End-to-End Models for Robust Speech Recognition

archiki/Robust-E2E-ASR 11 Feb 2021

A systematic comparison of these two approaches for end-to-end robust ASR has not been attempted before.

Interactive Feature Fusion for End-to-End Noise-Robust Speech Recognition

yuchen005/dpsl-asr 11 Oct 2021

Speech enhancement (SE) aims to suppress the additive noise from a noisy speech signal to improve the speech's perceptual quality and intelligibility.

Sequential Randomized Smoothing for Adversarially Robust Speech Recognition

raphaelolivier/smoothingasr EMNLP 2021

We apply adaptive versions of state-of-the-art attacks, such as the Imperceptible ASR attack, to our model, and show that our strongest defense is robust to all attacks that use inaudible noise, and can only be broken with very high distortion.