Gaussian Mixture Language Models for Speech Recognition Export

@proceedings{citeulike:1884182, abstract = {We propose a Gaussian mixture language model for speech recognition. Two potential benefits of using this model are smoothing unseen events, and ease of adaptation. It is shown how this model can be used alone or in conjunction with a a conventional N-gram model to calculate word probabilities. An interesting feature of the proposed technique is that many methods developed for acoustic models can be easily ported to GMLM. We developed two implementations of the proposed model for large vocabulary Arabic speech recognition with results comparable to conventional N-gram}, author = {Afify, M. and Siohan, O. and Sarikaya, R.}, booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on}, citeulike-article-id = {1884182}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ICASSP.2007.367155}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4218029}, doi = {10.1109/ICASSP.2007.367155}, journal = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on}, keywords = {gaussian, lm}, pages = {IV-29--IV-32}, posted-at = {2009-06-23 06:11:57}, priority = {0}, title = {Gaussian Mixture Language Models for Speech Recognition}, url = {http://dx.doi.org/10.1109/ICASSP.2007.367155}, volume = {4}, year = {2007} }

TY - CONF ID - citeulike:1884182 L3 - citeulike-article-id:1884182 N2 - We propose a Gaussian mixture language model for speech recognition. Two potential benefits of using this model are smoothing unseen events, and ease of adaptation. It is shown how this model can be used alone or in conjunction with a a conventional N-gram model to calculate word probabilities. An interesting feature of the proposed technique is that many methods developed for acoustic models can be easily ported to GMLM. We developed two implementations of the proposed model for large vocabulary Arabic speech recognition with results comparable to conventional N-gram JF - Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on EP - IV-32 TI - Gaussian Mixture Language Models for Speech Recognition VL - 4 T2 - Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on SP - IV-29 KW - gaussian KW - lm AU - Afify, M AU - Siohan, O AU - Sarikaya, R PY - 2007/// UR - http://dx.doi.org/10.1109/ICASSP.2007.367155 ER -