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2016, ISBN 0128023988, 308
Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against... 
Electronics & communications engineering | Automatic speech recognition
eBook
ACM Transactions on Intelligent Systems and Technology (TIST), ISSN 2157-6904, 07/2018, Volume 9, Issue 5, pp. 1 - 28
Journal Article
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), ISSN 2329-9290, 12/2016, Volume 24, Issue 12, pp. 2263 - 2276
Although great progress has been made in automatic speech recognition, significant performance degradation still exists in noisy environments. Recently, very... 
Convolution | robust speech recognition | Neural networks | Speech recognition | Noise robustness | acoustic modeling | Noise measurement | Convolutional neural networks | very deep CNNs | ACOUSTICS | MODEL | SPEAKER | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
Computer Speech & Language, ISSN 0885-2308, 2010, Volume 24, Issue 1, pp. 1 - 15
Robust speech recognition in everyday conditions requires the solution to a number of challenging problems, not least the ability to handle multiple sound... 
Speech separation | Speaker identification | Noise robustness | Auditory scene analysis | Simultaneous speech | Speech recognition | ROBUST | MASKING | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Analysis | Algorithms
Journal Article
Neurocomputing, ISSN 0925-2312, 09/2017, Volume 257, pp. 79 - 87
This paper addresses the robust speech recognition problem as a domain adaptation task. Specifically, we introduce an unsupervised deep domain adaptation (DDA)... 
Deep learning | Domain adaptation | Deep neural network | Robust speech recognition | NEURAL-NETWORKS | NOISE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Voice recognition | Neural networks
Journal Article
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), ISSN 2329-9290, 04/2014, Volume 22, Issue 4, pp. 826 - 835
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... 
Aurora-4 | feature mapping | time-frequency masking | robust ASR | deep neural networks | Training | Adaptation models | Noise | Speech | Feature extraction | Acoustics | Speech processing | Time-frequency masking | Deep neural networks | Feature mapping | Robust ASR | ACOUSTICS | INTELLIGIBILITY | ALGORITHM | BINARY | ENGINEERING, ELECTRICAL & ELECTRONIC | Neural networks | Discriminant analysis
Journal Article
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), ISSN 2329-9290, 12/2019, Volume 27, Issue 12, pp. 2080 - 2091
Journal Article
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), ISSN 2329-9290, 07/2016, Volume 24, Issue 7, pp. 1315 - 1329
Journal Article
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISSN 1520-6149, 05/2014, pp. 1759 - 1763
Denoising autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this... 
robust speech recognition | Noise reduction | Hidden Markov models | feature denoising | Speech recognition | Speech | Robustness | denoising autoencoder | Decoding | Noise measurement | deep neural network
Conference Proceeding
Computer Speech & Language, ISSN 0885-2308, 11/2017, Volume 46, pp. 535 - 557
•An analysis of the impact of acoustic mismatches between training and test data on the performance of robust ASR.•Including: environment, microphone and data... 
Speech enhancement | Microphone array | Train/test mismatch | Robust ASR | SOURCE SEPARATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Analysis | Electrical equipment and supplies industry | Signal and Image Processing | Computer Science
Journal Article
Journal Article
Computer Speech & Language, ISSN 0885-2308, 11/2017, Volume 46, pp. 605 - 626
•The presentation of a unique multi-microphone speech recognition challenge with speech recorded in real environments.•A detailed characterisation of the... 
‘CHiME’ challenge | Microphone array | Noise-robust ASR | 'CHiME' challenge | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Signal processing | Analysis | Electrical equipment and supplies industry | Signal and Image Processing | Computer Science
Journal Article
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISSN 1520-6149, 03/2012, pp. 4101 - 4104
This paper presents a new feature extraction algorithm called Power Normalized Cepstral Coefficients (PNCC) that is based on auditory processing. Major new... 
modulation filtering | Noise | rate-level curve | physiological modeling | Robust speech recognition | Mel frequency cepstral coefficient | temporal masking | on-line speech processing | Accuracy | feature extraction | medium-time power estimation | Speech recognition | Speech | asymmetric filtering | Reverberation | Speech processing
Conference Proceeding
IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, 11/2008, Volume 16, Issue 8, pp. 1662 - 1674
Journal Article
IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, 09/2011, Volume 19, Issue 7, pp. 1913 - 1924
Journal Article
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, ISSN 1520-6149, 05/2013, pp. 7092 - 7096
We propose a feature enhancement algorithm to improve robust automatic speech recognition (ASR). The algorithm estimates a smoothed ideal ratio mask (IRM) in... 
Computational Auditory Scene Analysis | Estimation | Speech recognition | Aurora-4 | Speech | Feature extraction | Robustness | instantaneous SNR | noise robust ASR | Signal to noise ratio
Conference Proceeding
IEEE/ACM Transactions on Audio, Speech, and Language Processing, ISSN 2329-9290, 04/2016, Volume 24, Issue 4, pp. 796 - 806
Journal Article
IEEE Journal of Selected Topics in Signal Processing, ISSN 1932-4553, 12/2017, Volume 11, Issue 8, pp. 1289 - 1300
Journal Article