Techniques for Noise Robustness in Automatic Speech Recognition
By Jort Florent Gemmeke, Ulpu Remes. 2012
141 citations recorded by the source corpus.
Subfields: Speech & Audio
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In Speech & Audio
Citations in the corpus
- CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, neutral): “Automatic speech recognition (ASR) performance in difficult reverberant and noisy conditions has improved tremendously in the last decade [1–6].”
- Improved cepstra minimum-mean-square-error noise reduction algorithm for robust speech recognition (2017, background): “INTRODUCTION Environment robustness in automatic speech recognition (ASR) remains a difficult problem despite many years of research [1][2][3][4][5][6][7][8].”
- Joint noise adaptive training for robust automatic speech recognition (2014, background): “With systems achieving acceptable performances in relatively clean conditions, focus on robustness has been sharpened.”
- An analysis of environment, microphone and data simulation mismatches in robust speech recognition (2017, unknown): “Speech enhancement and automatic speech recognition (ASR) in the presence of reverberation and nonstationary noise are still challenging tasks today (Baker et al., 2009; W¨olfel and McDonough, 2009; Virtanen et al., 2012; Li et al., 2015).”
- Data Augmentation for Robust Keyword Spotting under Playback Interference (2018, background): “Noise robustness techniques for speech recognition [8] and keyword spotting systems commonly fall into the categories of (a) signal or feature enhancement techniques that aim to remove the noise from the input features [9, 10] (b) model adaptation techniques that modify the model parameters to be…”
- Ideal ratio mask estimation using deep neural networks for robust speech recognition (2013, unknown): “Noise robust speech recognition is a widely studied research problem with important practical applications [1].”
- An investigation of deep neural networks for noise robust speech recognition (2013, methodology): “In the context of GMM-HMMs, this approach is referred to as feature-space noise adaptive training [20, 6] and this approach can be directly applied to DNN acoustic model.”