Robust automatic speech recognition : a bridge to practical application

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Large-Scale Domain Adaptation via Teacher-Student Learning (2017, methodology): “The target domain is a simulated noisy Cortana task which is obtained by digitally adding the ETSI noise [25] to the original Cortana data with SNRs from 5 to 20 dB.”
Recent progresses in deep learning based acoustic models (2017, background): “However, the performance dramatically degrades under mismatched or complicated environments such as higher noise condition, including music or interfering talkers, or speech with strong accents [95], [96].”
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks (2017, methodology): “To attain a satisfactory performance, SE has been used as a fundamental unit in various speech-related applications, such as automatic speech recognition [1], [2], speaker recognition”
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].”
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).”