Building a highly accurate Mandarin speech recognizer Export

@inproceedings{citeulike:4303418, abstract = {We describe a highly accurate large-vocabulary continuous Mandarin speech recognizer, a collaborative effort among four research organizations. Particularly, we build two acoustic models (AMs) with significant differences but similar accuracy for the purposes of cross adaptation and system combination. This paper elaborates on the main differences between the two systems, where one recognizer incorporates a discriminatively trained feature while the other utilizes a discriminative feature transformation. Additionally we present an improved acoustic segmentation algorithm and topic-based language model (LM) adaptation. Coupled with increased acoustic training data, we reduced the character error rate (CER) of the DARPA GALE 2006 evaluation set to 15.3\% from 18.4\%.}, author = {Hwang, Mei-Yuh and Peng, Gang and Wang, Wen and Faria, A. and Heidel, A. and Ostendorf, M.}, booktitle = {Automatic Speech Recognition \& Understanding, 2007. ASRU. IEEE Workshop on}, citeulike-article-id = {4303418}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ASRU.2007.4430161}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4430161}, doi = {10.1109/ASRU.2007.4430161}, journal = {Automatic Speech Recognition \& Understanding, 2007. ASRU. IEEE Workshop on}, keywords = {asr}, pages = {490--495}, posted-at = {2009-04-13 00:57:03}, priority = {2}, title = {Building a highly accurate Mandarin speech recognizer}, url = {http://dx.doi.org/10.1109/ASRU.2007.4430161}, year = {2007} }

TY - CONF ID - citeulike:4303418 L3 - citeulike-article-id:4303418 N2 - We describe a highly accurate large-vocabulary continuous Mandarin speech recognizer, a collaborative effort among four research organizations. Particularly, we build two acoustic models (AMs) with significant differences but similar accuracy for the purposes of cross adaptation and system combination. This paper elaborates on the main differences between the two systems, where one recognizer incorporates a discriminatively trained feature while the other utilizes a discriminative feature transformation. Additionally we present an improved acoustic segmentation algorithm and topic-based language model (LM) adaptation. Coupled with increased acoustic training data, we reduced the character error rate (CER) of the DARPA GALE 2006 evaluation set to 15.3% from 18.4%. JF - Automatic Speech Recognition & Understanding, 2007. ASRU. IEEE Workshop on EP - 495 TI - Building a highly accurate Mandarin speech recognizer T2 - Automatic Speech Recognition & Understanding, 2007. ASRU. IEEE Workshop on SP - 490 KW - asr AU - Hwang, Mei-Yuh AU - Peng, Gang AU - Wang, Wen AU - Faria, A AU - Heidel, A AU - Ostendorf, M PY - 2007/// UR - http://dx.doi.org/10.1109/ASRU.2007.4430161 ER -