Supervised speech separation based on deep learning: An overview
Speech separation is the task of separating target speech from background interference.
Traditionally, speech separation is studied as a signal processing problem. A more recent …
Traditionally, speech separation is studied as a signal processing problem. A more recent …
An overview of noise-robust automatic speech recognition
New waves of consumer-centric applications, such as voice search and voice interaction
with mobile devices and home entertainment systems, increasingly require automatic …
with mobile devices and home entertainment systems, increasingly require automatic …
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 …
recognition (ASR). The algorithm estimates a smoothed ideal ratio mask (IRM) in the Mel …
Towards scaling up classification-based speech separation
Formulating speech separation as a binary classification problem has been shown to be
effective. While good separation performance is achieved in matched test conditions using …
effective. While good separation performance is achieved in matched test conditions using …
The application of hidden Markov models in speech recognition
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 …
Hidden Markov Models in Speech Recognition Full text available at: http://dx.doi.org/10.1561/2000000004 …
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 …
missing data methods to increase robustness. The feature extraction, speaker modeling and …
An algorithm that improves speech intelligibility in noise for normal-hearing listeners
G Kim, Y Lu, Y Hu, PC Loizou - The Journal of the Acoustical Society of …, 2009 - pubs.aip.org
Traditional noise-suppression algorithms have been shown to improve speech quality, but
not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using …
not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using …
Exploring monaural features for classification-based speech segregation
Monaural speech segregation has been a very challenging problem for decades. By casting
speech segregation as a binary classification problem, recent advances have been made in …
speech segregation as a binary classification problem, recent advances have been made in …
Missing-feature approaches in speech recognition
In this article we have reviewed a wide variety of techniques based on the identification of
missing spectral features that have proved effective in reducing the error rates of automatic …
missing spectral features that have proved effective in reducing the error rates of automatic …
Binaural classification for reverberant speech segregation using deep neural networks
Speech signal degradation in real environments mainly results from room reverberation and
concurrent noise. While human listening is robust in complex auditory scenes, current …
concurrent noise. While human listening is robust in complex auditory scenes, current …