Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models

Untitled work

(2010) · Cited by 14,134 · see citing works

Untitled work

(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works

Kaldi Speech Recognition Toolkit

Povey, D. (2024) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.57702/jb3fvbn9 · Cited by 4,837 · see citing works

Front-End Factor Analysis for Speaker Verification

Dehak, N., Kenny, P. J., Dehak, R., Dumouchel, P., & Ouellet, P. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2064307 · Cited by 3,549 · see citing works

Hierarchical models of object recognition in cortex

Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309 · see citing works

Catastrophic forgetting in connectionist networks

French, R. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01294-2 · Cited by 2,311 · see citing works

Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models

Leggetter, C. J., & Woodland, P. C. (1995) · Computer Speech & Language · doi:10.1006/csla.1995.0010 · Cited by 2,210 · see citing works

Maximum likelihood linear transformations for HMM-based speech recognition

Gales, M. J. F. (1998) · Computer Speech & Language · doi:10.1006/csla.1998.0043 · Cited by 1,482 · see citing works

Connectionist Speech Recognition: A Hybrid Approach

Bourlard, H. A., & Morgan, N. H. (1993) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,115 · see citing works

Tandem connectionist feature extraction for conventional HMM systems

Heřmanský, H., Ellis, D. P. W., & Sharma, S. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2000.862024 · Cited by 697 · see citing works

An investigation of deep neural networks for noise robust speech recognition

Seltzer, M. L., Yu, D., & Wang, Y. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639100 · Cited by 658 · see citing works

Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription

Seide, F. T. B., Li, G., Xie, C., & Yu, D. (2011) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2011.6163899 · Cited by 641 · see citing works

Connectionist Speech Recognition

Bourlard, H. A., & Morgan, N. H. (1994) · Springer eBooks · doi:10.1007/978-1-4615-3210-1 · Cited by 587 · see citing works

Speaker adaptation of neural network acoustic models using i-vectors

Saon, G., Soltau, H., Nahamoo, D., & Picheny, M. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707705 · Cited by 586 · see citing works

KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition

Yu, D., Yao, K., Su, H., Li, G., & Seide, F. T. B. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639201 · Cited by 417 · see citing works

Probabilistic and Bottle-Neck Features for LVCSR of Meetings

Grézl, F., Karafiát, M., Kontar, S., & Černocký, J. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.367023 · Cited by 340 · see citing works

Pylearn2: a machine learning research libraryPreprint

Goodfellow, I., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., & Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.4214 · Cited by 245 · see citing works

Speaker adaptation of context dependent deep neural networks

Liao, H. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639212 · Cited by 233 · see citing works

Connectionist probability estimators in HMM speech recognition

Renals, S. J., Morgan, N., Bourlard, H., Cohen, M., & Franco, H. (1994) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.260359 · Cited by 228 · see citing works

Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminative learning of speaker code

Abdel‐Hamid, O., & Jiang, H. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639211 · Cited by 224 · see citing works

Adaptation of context-dependent deep neural networks for automatic speech recognition

Yao, K., Yu, D., Seide, F. T. B., Su, H., Deng, L., & Gong, Y. (2012) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2012.6424251 · Cited by 194 · see citing works

Auto-encoder bottleneck features using deep belief networks

Sainath, T. N., Kingsbury, B. E. D., & Ramabhadran, B. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288833 · Cited by 185 · see citing works

Speaker-adaptation for hybrid HMM-ANN continuous speech recognition system

Neto, J. P. D. S., Almeida, L. B., Hochberg, M. M., et al. (1995) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1995-411 · Cited by 181 · see citing works

Singular value decomposition based low-footprint speaker adaptation and personalization for deep neural network

Xue, J., Li, J., Yu, D., Seltzer, M., & Gong, Y. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854828 · Cited by 162 · see citing works

Linear hidden transformations for adaptation of hybrid ANN/HMM models

Gemello, R., Mana, F., Scanzio, S., Laface, P., & Mori, R. D. (2007) · Speech Communication · doi:10.1016/j.specom.2006.11.005 · Cited by 147 · see citing works

I-vector-based speaker adaptation of deep neural networks for French broadcast audio transcription

Gupta, V., Kenny, P. J., Ouellet, P., et al. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854823 · Cited by 121 · see citing works

Transcribing Meetings With the AMIDA Systems

Hain, T., Burget, L., Dines, J., et al. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2163395 · Cited by 121 · see citing works

Networks with trainable amplitude of activation functions

Trentin, E. (2001) · Neural Networks · doi:10.1016/s0893-6080(01)00028-4 · Cited by 107 · see citing works

Speaker adaptation using combined transformation and Bayesian methods

Digalakis, V., & Neumeyer, L. (1996) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.506933 · Cited by 101 · see citing works

iVector-based discriminative adaptation for automatic speech recognition

Karafiát, M., Burget, L., Matějka, P., et al. (2011) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2011.6163922 · Cited by 90 · see citing works

Catastrophic Forgetting in Connectionist Networks: Causes, Consequences and Solutions

French, R. M. (1999) · Cited by 75 · see citing works

Hermitian Polynomial for Speaker Adaptation of Connectionist Speech Recognition Systems

Siniscalchi, S. M., Li, J., & Lee, C. (2013) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2013.2270370 · Cited by 72 · see citing works

Connectionist speaker normalization and adaptation

Abrash, V., Franco, H., Sankar, A., & Cohen, M. (1995) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1995-414 · Cited by 70 · see citing works

Using neural network front-ends on far field multiple microphones based speech recognition

Liu, Y., Zhang, P., & Hain, T. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854663 · Cited by 68 · see citing works

Adaptation of deep neural network acoustic models using factorised i-vectors

Karanasou, P., Wang, Y., Gales, M., et al. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-488 · Cited by 64 · see citing works

Speaker Adaptive Training using Deep Neural Networks

Ochiai, T., Matsuda, S., Lu, X., et al. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854826 · Cited by 59 · see citing works

Revisiting hybrid and GMM-HMM system combination techniques

Świętojański, P., Ghoshal, A. K., & Renals, S. J. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638967 · Cited by 58 · see citing works

Investigation of maxout networks for speech recognition

Świętojański, P., Li, J., & Huang, J.-T. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6855088 · Cited by 52 · see citing works

Multi-level adaptive networks in tandem and hybrid ASR systems

Bell, P., Świętojański, P., & Renals, S. J. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639014 · Cited by 39 · see citing works

The IWSLT 2011 Evaluation Campaign on Automatic Talk Translation

Federico, M., Stüker, S., Bentivogli, L., Paul, M. D., Cettolo, M., Herrmann, T., Niehues, J., & Moretti, G. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5ms6auiwwnq2 · Cited by 37 · see citing works

A lecture transcription system combining neural network acoustic and language models

Bell, P., Yamamoto, H., Świętojański, P., et al. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-673 · Cited by 25 · see citing works

Personalization of end-to-end speech recognition on mobile devices for named entities

Sim, K. C., Beaufays, F., Benard, A., Guliani, D., Kabel, A., Khare, N., Lucassen, T., Zadrazil, P., Zhang, H., Johnson, L., et al. (2019) · 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · doi:10.1109/asru46091.2019.9003775 · Cited by 54 · see citing works

Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition

Geng, M., Xie, X., Ye, Z., Wang, T., Li, G., Hu, S., Liu, X., & Meng, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3195113 · Cited by 48 · see citing works

User adaptation of convolutional neural network for human activity recognition

Matsui, S., Inoue, N., Akagi, Y., Nagino, G., & Shinoda, K. (2017) · 2017 25th European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081308 · Cited by 46 · see citing works

On-device personalization of automatic speech recognition models for disordered speech

Tomanek, K., Beaufays, F., Cattiau, J., Chandorkar, A., & Sim, K. C. (2021) · arXiv · doi:10.48550/arxiv.2106.10259 · Cited by 14 · see citing works

A Methodology for Increasing Precision for the Efficient Implementation of Sigmoid Function

Suseendiran, S., Kumar, P. N., & Jose, D. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-2271-0_14

Describing function approximation for biomedical engineering applications

Kinnane, O. P. (2004) · Irish Signals and Systems Conference · doi:10.1049/cp:20040526 · Cited by 3 · see citing works

The nlogistic-sigmoid functionPreprint

Somefun, O. A., Akingbade, K. F., & Dahunsi, F. M. (2020) · arXiv (Cornell University)

Remarks on neural network controller using different sigmoid functions

Yamada, T., & Yabuta, T. (2002) · doi:10.1109/icnn.1994.374636 · Cited by 4 · see citing works

Efficient digital implementation of the sigmoid function for reprogrammable logic

Tommiska, M. T. (2003) · IEE Proceedings - Computers and Digital Techniques · doi:10.1049/ip-cdt:20030965 · Cited by 173 · see citing works