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: Speech/Non-Speech Segmentation Based on Phoneme Recognition Features | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 90495 Pages 1-13 DOI ASP 2006 90495 Speech Non-Speech Segmentation Based on Phoneme Recognition Features Janez Zibert Nikola PaveSiC and France Mihelic Faculty of Electrical Engineering University of Ljubljana Trzaska25 Ljubljana 1000 Slovenia Received 16 September 2005 Revised 7 February 2006 Accepted 18 February 2006 Recommended for Publication by Hugo Van hamme This work assesses different approaches for speech and non-speech segmentation of audio data and proposes a new high-level representation of audio signals based on phoneme recognition features suitable for speech non-speech discrimination tasks. Unlike previous model-based approaches where speech and non-speech classes were usually modeled by several models we develop a representation where just one model per class is used in the segmentation process. For this purpose four measures based on consonant-vowel pairs obtained from different phoneme speech recognizers are introduced and applied in two different segmentation-classification frameworks. The segmentation systems were evaluated on different broadcast news databases. The evaluation results indicate that the proposed phoneme recognition features are better than the standard mel-frequency cepstral coefficients and posterior probability-based features entropy and dynamism . The proposed features proved to be more robust and less sensitive to different training and unforeseen conditions. Additional experiments with fusion models based on cepstral and the proposed phoneme recognition features produced the highest scores overall which indicates that the most suitable method for speech non-speech segmentation is a combination of low-level acoustic features and high-level recognition features. Copyright 2006 Janez Zibert et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use .
