An overview of noise-robust automatic speech recognition

J Li, L Deng, Y Gong… - IEEE/ACM Transactions …, 2014 - ieeexplore.ieee.org
New waves of consumer-centric applications, such as voice search and voice interaction
with mobile devices and home entertainment systems, increasingly require automatic …

An overview of speaker identification: Accuracy and robustness issues

R Togneri, D Pullella - IEEE circuits and systems magazine, 2011 - ieeexplore.ieee.org
This paper presents the main paradigms for speaker identification, and recent work on
missing data methods to increase robustness. The feature extraction, speaker modeling and …

Ideal ratio mask estimation using deep neural networks for robust speech recognition

A Narayanan, DL Wang - 2013 IEEE international conference …, 2013 - ieeexplore.ieee.org
We propose a feature enhancement algorithm to improve robust automatic speech
recognition (ASR). The algorithm estimates a smoothed ideal ratio mask (IRM) in the Mel …

The application of hidden Markov models in speech recognition

M Gales, S Young - Foundations and Trends® in Signal …, 2008 - nowpublishers.com
The Application of Hidden Markov Models in Speech Recognition Page 1 The Application of
Hidden Markov Models in Speech Recognition Full text available at: http://dx.doi.org/10.1561/2000000004 …

Hawkes processes for events in social media

MA Rizoiu, Y Lee, S Mishra, L Xie - Frontiers of multimedia research, 2017 - dl.acm.org
This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …

Making machines understand us in reverberant rooms: Robustness against reverberation for automatic speech recognition

T Yoshioka, A Sehr, M Delcroix… - IEEE Signal …, 2012 - ieeexplore.ieee.org
Speech recognition technology has left the research laboratory and is increasingly coming
into practical use, enabling a wide spectrum of innovative and exciting voice-driven …

Exemplar-based sparse representations for noise robust automatic speech recognition

JF Gemmeke, T Virtanen… - IEEE Transactions on …, 2011 - ieeexplore.ieee.org
This paper proposes to use exemplar-based sparse representations for noise robust
automatic speech recognition. First, we describe how speech can be modeled as a linear …

Deep learning for video classification and captioning

Z Wu, T Yao, Y Fu, YG Jiang - Frontiers of multimedia research, 2017 - dl.acm.org
Deep learning for video classification and captioning Page 1 IPART MULTIMEDIA
CONTENT ANALYSIS Page 2 Page 3 1Deep Learning for Video Classification and …

Investigation of speech separation as a front-end for noise robust speech recognition

A Narayanan, DL Wang - IEEE/ACM Transactions on Audio …, 2014 - ieeexplore.ieee.org
Recently, supervised classification has been shown to work well for the task of speech
separation. We perform an in-depth evaluation of such techniques as a front-end for noise …

Compressive sensing for missing data imputation in noise robust speech recognition

JF Gemmeke, H Van Hamme… - IEEE Journal of …, 2010 - ieeexplore.ieee.org
An effective way to increase the noise robustness of automatic speech recognition is to label
noisy speech features as either reliable or unreliable (missing), and to replace (impute) the …