Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/15314
Title: Combining Novel Acoustic Features using SVM to Detect Speaker Changing Points
Contributor(s): Zhong, Haishan (author); Cho, David (author); Pervouchine, Vladimir (author); Leedham, Graham  (author)
Publication Date: 2008
Handle Link: https://hdl.handle.net/1959.11/15314
Abstract: Automatic speaker change point detection segments different speakers from continuous speech according to speaker characteristics. This is often a necessary step before applying speaker verification or identification systems. Among the features to represent a speaker in the speaker change point detection systems acoustic features are commonly used. Commonly used features are Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC). However, the features are affected by speech content, environment, type of recording device, etc. So far, no features have been discovered, which values depend only on the speaker. In this paper four novel feature types proposed in recent major journals and conference papers for speaker verification problem, are applied to the problem of speaker change point detection. The features are also used to form a combination scheme via SVM classifier. The results shows that the proposed scheme improves the performance of speaker changing point detection as compared to the system that uses MFCC features. It was also found that some of the novel features of low dimensionality give comparable speaker change point detection accuracy to the high-dimensional MFCC features.
Publication Type: Conference Publication
Conference Details: BIOSIGNALS 2008: International Conference on Bio-inspired Systems and Signal Processing, Funchal, Portugal, 28th - 31st January, 2008
Source of Publication: Proceedings of the First International Conference on Biomedical Electronics and Devices (BIOSIGNALS 2008), v.1, p. 224-227
Publisher: Institute for Systems and Technologies of Information, Control and Communication (INSTICC)
Place of Publication: United States of America
Fields of Research (FoR) 2008: 080106 Image Processing
080109 Pattern Recognition and Data Mining
080104 Computer Vision
Socio-Economic Objective (SEO) 2008: 810107 National Security
810199 Defence not elsewhere classified
890299 Computer Software and Services not elsewhere classified
HERDC Category Description: E2 Non-Refereed Scholarly Conference Publication
Publisher/associated links: http://www.biosignals.biostec.org/Abstracts/2008/BIOSIGNALS_2008_Abstracts.htm
Appears in Collections:Conference Publication
School of Science and Technology

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