tailieunhanh - Báo cáo hóa học: " Research Article A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector"

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 A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2007 Article ID 43218 7 pages doi 2007 43218 Research Article A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector H. Othman and T. Aboulnasr School of Information Technology and Engineering Faculty of Engineering University of Ottawa Ontario Canada K1N 6N5 Received 15 December 2005 Revised 13 November 2006 Accepted 28 November 2006 Recommended by Thippur V. Sreenivas We introduce an efficient hidden Markov model-based voice activity detection VAD algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T parameters with no additional cost for feature extraction the proposed algorithm significantly outperforms Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD option 2 AMR2 . Copyright 2007 H. Othman and T. Aboulnasr. 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 Actual speech activities normally occupy 60 of the time of a regular conversation in a telecommunication system 1 . Voice activity detection VAD enables reallocating resources during the periods of speech absence. In modern telecommunication systems VADs in conjunction with comfort noise generator CNG and discontinuous transmission DTX modules play a critical role in enhancing the system performance. A VAD distinguishes between speech and nonspeech frames in the presence of background noise. In general VAD errors can be categorized into two main types of errors .