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Báo cáo hóa học: " Research Article Robust Speech Recognition Using Factorial HMMs for Home Environments"

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 Robust Speech Recognition Using Factorial HMMs for Home Environments | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 20593 9 pages doi 10.1155 2007 20593 Research Article Robust Speech Recognition Using Factorial HMMs for Home Environments Agnieszka Betkowska Koichi Shinoda and Sadaoki Furui Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Received 1 February 2006 Revised 19 August 2006 Accepted 17 December 2006 Recommended by Mark Gales We focus on the problem of speech recognition in the presence of nonstationary sudden noise which is very likely to happen in home environments. As a model compensation method for this problem we investigated the use of factorial hidden Markov model FHMM architecture developed from a clean-speech hidden Markov model HMM and a sudden-noise HMM. While in conventional studies this architecture is defined only for static features of the observation vector we extended it to dynamic features. In addition we performed home-environment adaptation of FHMMs to the characteristics of a given house. A database recorded by a personal robot called PaPeRo in home environments was used for the evaluation of the proposed method. Isolated word recognition experiments demonstrated the effectiveness of the proposed method under noisy conditions. Home-dependent word FHMMs HD-FHMMs reduced the word error rate by 20.5 compared to that of the clean-speech word HMMs. Copyright 2007 Agnieszka Betkowska et al. 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. 1. INTRODUCTION A great deal of effort has been devoted to developing personal robots such as household robots educational robots or personal assistants that interact with human beings in the home environment. Most of those robots are equipped with a speech-recognition .