Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Phoneme and Sentence-Level Ensembles for Speech Recognition | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2011 Article ID 426792 17 pages doi 2011 426792 Research Article Phoneme and Sentence-Level Ensembles for Speech Recognition Christos Dimitrakakis1 and Samy Bengio2 1FIAS Ruth-Moufang-Strf 1 60438 Frankfurt Germany 2 Google 1600 Amphitheatre Parkway B1350-138 Mountain View CA 94043 USA Correspondence should be addressed to Christos Dimitrakakis Received 17 September 2010 Accepted 20 January 2011 Academic Editor Elmar Noth Copyright 2011 C. Dimitrakakis and S. Bengio. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this we compare two different boosting schemes one at the phoneme level and one at the utterance level with a phoneme-level bagging scheme. We control for many parameters and other choices such as the state inference scheme used. In an unbiased experiment we clearly show that the gain of boosting methods compared to a single hidden Markov model is in all cases only marginal while bagging significantly outperforms all other methods. We thus conclude that bagging methods which have so far been overlooked in favour of boosting should be examined more closely as a potentially useful ensemble learning technique for speech recognition. 1. Introduction This paper examines the application of ensemble methods to hidden Markov models HMMs for speech recognition. We consider two methods bagging and boosting. Both methods feature a fixed mixing distribution between the ensemble components which simplifies the inference though it does not completely trivialise it. This paper follows up on and consolidates previous results 1-3 that focused on boosting. The main .
