Universal attribute characterization of spoken languages for automatic spoken language recognition
Tài liệu tham khảo
Adami, 2003, Segmentation of speech for speaker and language recognition, 841
Adda-Decker, 2003, Phonetic knowledge, phonotactics and perceptual validation for automatic language identification, 747
Allen, 1994, How do humans process and recognize speech, IEEE Transactions on Speech and Audio Processing, 2, 567, 10.1109/89.326615
Bellegarda, 2000, Exploiting latent semantic information in statistical language modeling, Proceedings of the IEEE, 88, 1279, 10.1109/5.880084
Berkling, 1994, Analysis of phoneme-based features for language identification
Campbell, 2005, Support vector machines for speaker and language recognition, Computer Speech and Language, 20, 210
Campbell, 2006, Support vector machines using GMM supervectors for speaker recognition, IEEE Signal Processing Letters, 13, 308, 10.1109/LSP.2006.870086
Corredor-Ardoy, 1997, A multilingual phoneme and model set: towards a universal base for automatic speech recognition, 355
Davis, 1980, Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, IEEE Transactions on Acoustics Speech and Signal Processing, 28, 357, 10.1109/TASSP.1980.1163420
Dehak, 2010, Front-end factor analysis for speaker verification, IEEE Transactions on Audio, Speech and Language Processing, 19, 788, 10.1109/TASL.2010.2064307
Deng, 2011, Deep convex network: a scalable architecture for deep learning, 2285
Gao, 2006, A maximal figure-of-merit (MFoM)-learning approach to robust classifier design for text categorization, ACM Transactions on Information Systems, 24, 190, 10.1145/1148020.1148022
Gauvain, 2000, Large-vocabulary continuous speech recognition: advances and applications, Proceedings of the IEEE, 88, 1181, 10.1109/5.880079
Hazen, T.J., 1993. Automatic language identification using a segment-based approach. Ph.D. Thesis, M.S. Thesis. Mass. Inst. Technol., Cambridge, MA, USA.
Katagiri, 1998, Pattern recognition using a family of design algorithms based upon generalized probability descent method, Proceedings of the IEEE, 86, 2345, 10.1109/5.726793
Kirchhoff, K., 1999. Robust speech recognition using articulatory information. Ph.D. Thesis. University of Bielefeld, Germany.
Kirchhoff, 2002, Mixed-memory Markov models for automatic language identification
Lee, 2004, From knowledge-ignorant to knowledge-rich modeling: a new speech research paradigm for next generation automatic speech recognition, 109
Lee, 2000, On adaptive decision rules and decision parameter adaptation for automatic speech recognition, Proceedings of the IEEE, 88, 1241, 10.1109/5.880082
Lee, 1988, A segment model based approach to speech recognition, 501
1996
Li, 2007, A vector space modeling approach to spoken language identification, IEEE Transactions on Audio, Speech and Language Processing, 15, 271, 10.1109/TASL.2006.876860
Martin, 2006, The current state of language recognition: NIST 2005 evaluation results, 1
Martin, 2003, NIST 2003 language recognition evaluation, 1341
Martinez, 2011, Language recognition in iVectors space, 861
Matrouf, 1998, Language identification incorporating lexical information
Matějka, 2005, Phonotactic language identification using high quality phoneme recognition, 2237
Mohamed, 2009, Deep belief networks for phone recognition
Muthusamy, 1992, The OGI multi-language telephone speech corpus, 895
Muthusamy, 1994, Perceptual benchmarks for automatic language identification
Rabiner, 1999, A tutorial on hidden Markov models and selected application in speech recognition, Proceedings of the IEEE, 77, 257, 10.1109/5.18626
Rabiner, 1993
Salton, 1971
Schwarz, 2006, Hierarchical structures of neural networks for phoneme recognition, 325
Seide, 2011, Conversational speech transcription using context-dependent deep neural networks, 437
Singer, 2003, Acoustic, phonetic, and discriminative approaches to automatic language recognition, 1345
Siniscalchi, 2012, Experiments on cross-language attribute detection and phone recognition with minimal target specific training data, IEEE Transactions on Audio, Speech and Language Processing, 20, 875, 10.1109/TASL.2011.2167610
Siniscalchi, 2010, Exploiting context-dependency and acoustic resolution of universal speech attribute models in spoken language recognition, 2718
Siniscalchi, 2009, Exploring universal attribute characterization of spoken languages for spoken language recognition, 168
Siniscalchi, 2008, Toward a detector-based universal phone recognizer, 4261
Soufifar, 2011, iVector approach to phonotactic language recognition, 2913
Stüker, 2003, Multilingual articulatory features
Sugiyama, 1991, Automatic language recognition using acoustic features, 813
Torres-Carrasquillo, 2002, Approaches to language identification using Gaussian mixture models and shifted delta cepstral features, 89
Yu, 2012, Boosting attribute and phone estimation accuracy with deep neural networks for detection-based speech recognition, 4169, 10.1109/ICASSP.2012.6288837
Zissman, 1996, Comparison of four approaches to automatic language identification of telephone speech, IEEE Transactions on Speech and Audio Processing, 4, 31, 10.1109/TSA.1996.481450