| Power-normalized cepstral coefficients (PNCC) for robust speech recognition C Kim, RM Stern IEEE/ACM Transactions on audio, speech, and language processing 24 (7), 1315 …, 2016 | 638 | 2016 |
| Multichannel signal processing with deep neural networks for automatic speech recognition TN Sainath, RJ Weiss, KW Wilson, B Li, A Narayanan, E Variani, ... IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP) 25 …, 2017 | 269 | 2017 |
| Generation of large-scale simulated utterances in virtual rooms to train deep-neural networks for far-field speech recognition in Google Home C Kim, A Misra, K Chin, T Hughes, A Narayanan, T Sainath, M Bacchiani INTERSPEECH 2017, 2017 | 264 | 2017 |
| Robust signal-to-noise ratio estimation based on waveform amplitude distribution analysis C Kim, RM Stern INTERSPEECH 2008 (Ninth Annual Conference of the International Speech …, 2008 | 209 | 2008 |
| Acoustic Modeling for Google Home B Li, T Sainath, A Narayanan, J Caroselli, M Bacchiani, A Misra, I Shafran, ... INTERSPEECH 2017, 2017 | 202 | 2017 |
| Sound source estimation using neural networks C Kim, RC Nongpiur, A Narayanan US Patent 10,063,965, 2018 | 186 | 2018 |
| Delta-spectral cepstral coefficients for robust speech recognition K Kumar, C Kim, RM Stern ICASSP 2011: IEEE international conference on acoustics, speech and signal …, 2011 | 155 | 2011 |
| Feature extraction for robust speech recognition based on maximizing the sharpness of the power distribution and on power flooring C Kim, RM Stern ICASSP 2010: IEEE International Conference on Acoustics, Speech and Signal …, 2010 | 150 | 2010 |
| Feature extraction for robust speech recognition using a power-law nonlinearity and power-bias subtraction C Kim, RM Stern INTERSPEECH 2009 (Tenth Annual Conference of the International Speech …, 2009 | 117 | 2009 |
| Mobile device and method for preventing undesired key depression in the same C Kim US Patent 7,602,377, 2009 | 102 | 2009 |
| Attention based on-device streaming speech recognition with large speech corpus K Kim, K Lee, D Gowda, J Park, S Kim, S Jin, YY Lee, J Yeo, D Kim, ... ASRU 2019 : IEEE Workshop on Automatic Speech Recognition & Understanding, 2019 | 63 | 2019 |
| Apparatus and method for reducing power consumption in a mobile communication terminal C Kim US Patent App. 10/928,673, 2005 | 63 | 2005 |
| Signal separation for robust speech recognition based on phase difference information obtained in the frequency domain C Kim, K Kumar, B Raj, RM Stern INTERSPEECH 2009 (Tenth Annual Conference of the International Speech …, 2009 | 60 | 2009 |
| Nonlinear enhancement of onset for robust speech recognition C Kim, RM Stern INTERSPEECH 2010 (Eleventh Annual Conference of the International Speech …, 2010 | 56 | 2010 |
| End-end Speech-to-Text Translation with Modality Agnostic Meta-Learning S Indurthi, H Han, NK Lakumarapu, B Lee, I Chung, S Kim, C Kim ICASSP 2020: IEEE International Conference on Acoustics, Speech and Signal …, 2020 | 54 | 2020 |
| Improved vocal tract length perturbation for a state-of-the-art end-to-end speech recognition system C Kim, M Shin, A Garg, D Gowda INTERSPEECH 2019, 739-743, 2019 | 48 | 2019 |
| Signal processing for robust speech recognition motivated by auditory processing C Kim Ph. D. Thesis, Carnegie Mellon University, School of Computer Science, 2010 | 43 | 2010 |
| Robust DTW-based recognition algorithm for hand-held consumer devices C Kim, K Seo IEEE Transactions on Consumer Electronics 51 (2), 699-709, 2005 | 42 | 2005 |
| A review of on-device fully neural end-to-end automatic speech recognition algorithms C Kim, D Gowda, D Lee, J Kim, A Kumar, S Kim, A Garg, C Han ACSSC 2020: Asilomar Conference on Signals, Systems, and Computers, 2020 | 39 | 2020 |
| Physiologically-motivated synchrony-based processing for robust automatic speech recognition C Kim, YH Chiu, RM Stern INTERSPEECH 2006 (Ninth International Conference on Spoken Language Processing), 2006 | 39 | 2006 |
Richard M. SternProfessor of Electrical Engineering and Computer Science, Carnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
