Background of robust speech recognition
暂无分享,去创建一个
[1] Brendan J. Frey,et al. ALGONQUIN: iterating laplace's method to remove multiple types of acoustic distortion for robust speech recognition , 2001, INTERSPEECH.
[2] Yongqiang Wang,et al. An investigation of deep neural networks for noise robust speech recognition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[3] Li Deng,et al. Dynamic compensation of HMM variances using the feature enhancement uncertainty computed from a parametric model of speech distortion , 2005, IEEE Transactions on Speech and Audio Processing.
[4] Li Deng,et al. A Bayesian approach to speech feature enhancement using the dynamic cepstral prior , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[5] Janet M. Baker,et al. The Design for the Wall Street Journal-based CSR Corpus , 1992, HLT.
[6] Jacob Benesty,et al. Spectral Enhancement Methods , 2009 .
[7] David Pearce,et al. The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions , 2000, INTERSPEECH.
[8] Stan Davis,et al. Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .
[9] Li Deng,et al. MiPad: a multimodal interaction prototype , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[10] Reinhold Häb-Umbach,et al. An analytic derivation of a phase-sensitive observation model for noise robust speech recognition , 2009, INTERSPEECH.
[11] Reinhold Häb-Umbach,et al. A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust Speech Recognition , 2008, IEEE Transactions on Audio, Speech, and Language Processing.
[12] Ning Ma,et al. The CHiME corpus: a resource and a challenge for computational hearing in multisource environments , 2010, INTERSPEECH.
[13] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.
[14] Yifan Gong,et al. Learning small-size DNN with output-distribution-based criteria , 2014, INTERSPEECH.
[15] Jon Barker,et al. The second ‘chime’ speech separation and recognition challenge: Datasets, tasks and baselines , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[16] Dong Yu,et al. Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[17] Chin-Hui Lee,et al. On stochastic feature and model compensation approaches to robust speech recognition , 1998, Speech Commun..
[18] Alejandro Acero,et al. Acoustical and environmental robustness in automatic speech recognition , 1991 .
[19] Jinyu Li,et al. Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks. , 2013, ICLR 2013.
[20] Li Deng,et al. Estimating cepstrum of speech under the presence of noise using a joint prior of static and dynamic features , 2004, IEEE Transactions on Speech and Audio Processing.
[21] H. Bourlard,et al. Interpretation of Multiparty Meetings the AMI and Amida Projects , 2008, 2008 Hands-Free Speech Communication and Microphone Arrays.
[22] Khalid Choukri,et al. SPEECHDAT-CAR. A Large Speech Database for Automotive Environments , 2000, LREC.
[23] Li Deng,et al. A comparison of three non-linear observation models for noisy speech features , 2003, INTERSPEECH.
[24] Xiao Li,et al. Machine Learning Paradigms for Speech Recognition: An Overview , 2013, IEEE Transactions on Audio, Speech, and Language Processing.
[25] Khalid Choukri,et al. The CHIL audiovisual corpus for lecture and meeting analysis inside smart rooms , 2007, Lang. Resour. Evaluation.
[26] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[27] Li Deng,et al. Enhancement of log Mel power spectra of speech using a phase-sensitive model of the acoustic environment and sequential estimation of the corrupting noise , 2004, IEEE Transactions on Speech and Audio Processing.
[28] Erich Elsen,et al. Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.
[29] Reinhold Häb-Umbach. Uncertainty Decoding and Conditional Bayesian Estimation , 2011, Robust Speech Recognition of Uncertain or Missing Data.
[30] Brendan J. Frey,et al. ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition , 2001, NIPS.
[31] Li Deng,et al. Front-End, Back-End, and Hybrid Techniques for Noise-Robust Speech Recognition , 2011, Robust Speech Recognition of Uncertain or Missing Data.
[32] Emmanuel Vincent,et al. An investigation of likelihood normalization for robust ASR , 2014, INTERSPEECH.
[33] Jeff A. Bilmes,et al. The design and collection of COSINE, a multi-microphone in situ speech corpus recorded in noisy environments , 2012, Comput. Speech Lang..
[34] E. A. Martin,et al. Multi-style training for robust isolated-word speech recognition , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.